add research proposal
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Arthur Grisel-Davy
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PhD/research_proposal/bibliography.bib
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PhD/research_proposal/bibliography.bib
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PhD/research_proposal/conclusion.tex
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PhD/research_proposal/conclusion.tex
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\chapter{Conclusion}
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The problem of leveraging power side-channel analysis to defend embedded system present unique capabilities.
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In opposition with current common \gls{ids} techniques, physics-based security is not built on purpose-made actionable data.
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The very nature of the input information sets this techniques aside.
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Power consumption is closely related to instruction execution, and makes it a good proxy variable for machine activity.
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Moreover, power is easy and cheap to measures reliably at a high sampling rate, enabling analysis of any machine consuming electricity.
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Finally, a sequence of instructions is generally related to a unique power consumption pattern.
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This \textit{one-to-one} relationship, allows to consider the power consumption as a signature for a software of machine activity.
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However, power consumtion is not an actionable information.
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From the raw time series format, little can be extracted about the machine activity or integrity.
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To enable further analysis, a set of algorithm is required for both runtime online analysis as well as offline monitoring of specific activity.
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The full range of capabilities is still unknown.
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Successfull runtime monitoring enables the detection of activity policy violation, anomalous activity detection, machine failure detection or distributed attacks.
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On the other hand, pre-OS monitoring enable the detection of boot process violation at a level where common \gls{ids} are not enabled yet.
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These are just some of the possible applciation of this technology, with many more to be discovered.
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With this proposal, I present some problems to study that enables some of the applications of physics-based security.
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I hereby ask to continue researching these problems with the view to complete my PhD thesis.
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PhD/research_proposal/frontpages.tex
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PhD/research_proposal/frontpages.tex
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% T I T L E P A G E
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% -------------------
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% Last updated August 16, 2022, by IST-Client Services
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% The title page is counted as page `i' but we need to suppress the
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% page number. Also, we don't want any headers or footers.
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\pagestyle{empty}
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\pagenumbering{roman}
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% The contents of the title page are specified in the "titlepage"
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% environment.
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\begin{titlepage}
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\begin{center}
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\vspace*{1.0cm}
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\Huge
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{\bf \mytopic}
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\vspace*{1.0cm}
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\normalsize
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by \myauthor\\
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\vspace*{1.0cm}
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\Large
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\author \\
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\vspace*{3.0cm}
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\normalsize
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A research proposal\\
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presented to the University of Waterloo \\
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for fulfillment of the \\
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thesis requirement for the degree of \\
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Doctor of Philosophy \\
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in \\
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Philosophy of Electrical and Computer Engineering \\
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\vspace*{2.0cm}
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Waterloo, Ontario, Canada \\
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\vspace*{1.0cm}
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\copyright \myauthor \\
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\end{center}
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\end{titlepage}
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% The rest of the front pages should contain no headers and be numbered using Roman numerals starting with `ii'
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\pagestyle{plain}
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\setcounter{page}{2}
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\cleardoublepage % Ends the current page and causes all figures and tables that have so far appeared in the input to be printed.
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% In a two-sided printing style, it also makes the next page a right-hand (odd-numbered) page, producing a blank page if necessary.
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\phantomsection % allows hyperref to link to the correct page
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% E X A M I N I N G C O M M I T T E E (Required for Ph.D. theses only)
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% Remove or comment out the lines below to remove this page
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\addcontentsline{toc}{chapter}{Examining Committee}
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\begin{center}\textbf{Examining Committee Membership}\end{center}
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\noindent
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The following served on the Examining Committee for this thesis. The decision of the Examining Committee is by majority vote.
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\bigskip
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\noindent
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\begin{tabbing}
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Internal-External Member: \= \kill % using longest text to define tab length
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Internal Mamber: \> Mark Crowley \\
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\> Professor, Dept. Electrical and Computer Engineering\\
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\> University of Waterloo \\
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\end{tabbing}
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\bigskip
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\noindent
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\begin{tabbing}
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Internal-External Member: \= \kill % using longest text to define tab length
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Internal Member: \> Catherine Gebotys\\
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\> Professor, Dept. Electrical and Computer Engineering\\
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\> University of Waterloo \\
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\end{tabbing}
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\bigskip
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\noindent
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\begin{tabbing}
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Internal-External Member: \= \kill % using longest text to define tab length
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Internal Member: \> Patrick Mitran\\
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\> Professor, Dept. Electrical and Computer Engineering\\
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\> University of Waterloo \\
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\end{tabbing}
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\bigskip
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\noindent
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\begin{tabbing}
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Internal-External Member: \= \kill % using longest text to define tab length
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Supervisor: \> Sebastian Fischmeister \\
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\> Professor, Dept. Electrical and Computer Engineering\\
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\> University of Waterloo \\
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\end{tabbing}
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\bigskip
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\noindent
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\cleardoublepage
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\phantomsection % allows hyperref to link to the correct page
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% A B S T R A C T
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% ---------------
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\addcontentsline{toc}{chapter}{Abstract}
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\begin{center}\textbf{Abstract}\end{center}
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This is the abstract.
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\cleardoublepage
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\phantomsection % allows hyperref to link to the correct page
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% T A B L E O F C O N T E N T S
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% ---------------------------------
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\renewcommand\contentsname{Table of Contents}
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\tableofcontents
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\cleardoublepage
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\phantomsection % allows hyperref to link to the correct page
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% L I S T O F F I G U R E S
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% -----------------------------
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\addcontentsline{toc}{chapter}{List of Figures}
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\listoffigures
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\cleardoublepage
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\phantomsection % allows hyperref to link to the correct page
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% L I S T O F T A B L E S
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% ---------------------------
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\addcontentsline{toc}{chapter}{List of Tables}
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\listoftables
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\cleardoublepage
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\phantomsection % allows hyperref to link to the correct page
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% L I S T O F A B B R E V I A T I O N S
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% ---------------------------
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%\renewcommand*{\abbreviationsname}{List of Abbreviations}
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%\printglossary[type=abbreviations]
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%\cleardoublepage
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%\phantomsection % allows hyperref to link to the correct page
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% Change page numbering back to Arabic numerals
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\pagenumbering{arabic}
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PhD/research_proposal/futurwork.tex
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PhD/research_proposal/futurwork.tex
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\chapter{Planned Work}\label{chap:futurwork}
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All the work achieved in the preliminary work serves as the foundation for the planned work.
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The thesis will focus on the state detection problem.
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Detecting the state of a system is a stepping stone in the construction of specialized tools.
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As illustrated by the \gls{sds} and \gls{bpv}, the detection of specific attacks often relies on the ability to pre-process the time series to find sections of interest.
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In this sense, solving the state detection problem enables a deeper investigation of power consumption by making the data actionable.
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The different machines and data measurement design lead to different problems to solve and different detection capabilities.
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This chapter described the planned research topics with their problem statement and a description of the motivation and expected results.
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\section{Single-Source, Single-Measure}
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The \gls{dsd} shows promising results in an experimental setup.
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To this date, the experiments have focused on the detection of simple global states.
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The global state are usualy \textit{OFF}, \textit{ON}, \textit{BOOT}, \textit{HIGH LOAD}.
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Depending on the machine, other states like \textit{FIRMWARE FLASH}, \textit{SLEEP} or a specific activity mode can also be detected.
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The experiments focus on the deployment of general-purpose computers, network switches, and \gls{wap}/routers.
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In the next months, the goal for the \gls{dsd} is to evaluate the performances of the runtime state detection.
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The current accuracy and edit distance performances (see Figure \ref{fig:dsd_acc}) show promissing results for the detection of well defined states (i.e. states associated with a striking variation of average power consumption).
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However, in order for the \gls{dsd} to provide useful and reliable runtime labeling of the a machine's activity, a more diverse selection of states must be evaluated for typical machines like general purpose computers.
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These results will be compiled for the publication of an article.
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The work on \gls{dsd} is the base for the planned development of more specific applications of the same principle of physics-based monitoring.
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\begin{figure}
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\centering
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\includegraphics[width=\textwidth]{images/dsd_acc}
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\caption{Current results of the DSD algorithm on several datasets..}
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\label{fig:dsd_acc}
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\end{figure}
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\section{Single-Source, Multi-Measure}
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The global power consumption of a machine does not necessarily tell the full story about its activity.
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In an embedded system, the power consumption can be assigned to different components, each with its specific activity.
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For the simplest systems performing one specific task \agd{give examples}, the activity of each component is often consistently correlated/agd{weird sentence}.
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If the system is in a mode \textit{A} then each component is in mode \textit{A}, and the global power consumption will display the \textit{mode A} pattern.
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For more complex systems, different components can be in different modes to accommodate the multi-tasking nature of the global activity.
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In this case, if the first component is in mode \textit{A} but the second is in mode \textit{B}, this indicates a different global activity than if both are in the same mode.
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For example, if the bootup sequence of a general-purpose computer shows a high \gls{cpu} activity but a low storage activity, it could indicate a failure to boot or an attacker booting the system from external storage.
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Access to each component's individual power consumption opens the way to a more granular understanding of the machine's activity.
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However, the new nature of the captured data requires an evolution of the techniques developed before.
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\subsection{Problem Statement}
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Differentiating between the different components to better understand the activity of a machine is a valuable capability associated with a new problem.
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\begin{problem-statement}[Single-Source Multi-Measure]
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Given a discretized time series $t$ and a set of $n$ components for each of $m$ patterns $P=\{\{\chi\},\{P_{11},\dots, P_{1n}\},\dots, \{P_{m1},\dots, P_{mn}\}\}$, identify an injective mapping $m_{SSMM}:\mathbb{N}\longrightarrow P$ such that every sample $t[i]$
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maps to exactly one set of pattern components in $P$ with the condition that the sample matches an occurrence of the set of patterns in $t$.
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\end{problem-statement}
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The time series $t$ is a discretized, multivariate, real-valued time series.
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Each sample $t[i]$ is a vector or $n$ component representing the value of each dimension of $t$ at a point in time.
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Each pattern in $P$ contain multiple pattern components and represent a global pattern across all dimension of $t$.
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Thus, the number of components of each pattern must be equal to the dimensions of $t$.
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\subsection{Applications}
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The goal of the multi-measure setup is dual.
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First, correlated information allows for a more robust detection mechanism.
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If all components of a machine display behaviours associated with the same global activity, the detection confidence is greater than with the global consumption only.
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Second, multiple measures enable a more granular activity detection.
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With the power consumption measurement of multiple components available, every combination of component's activity can be associated with a different global activity.
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These changes allow for detecting potentially anomalous combinations of states and for a better understanding of the machine's behaviour.
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The typical application of this technology would concern general-purpose computers or medium-complexity systems with multiple internal components.
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These machines are typically difficult to profile with global consumption as each component influences the measure in a different way.
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The detection of the activity can be restricted to general states like \textit{ON}, \textit{OFF}, \textit{SLEEP} or \textit{HIGH LOAD}.
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While this information is still valuable, it does not enable in-depth monitoring of the machine.
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\section{Multi-Source Single-Measure}
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If the Single-Source Multi-Measure was looking \textit{in} a machine to get more insight, the Multi-Source Single-Measure is looking \textit{out} and considering multiple devices at once.
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In a context where measuring the consumption of individual machines is not possible, the problem of disambiguation arises.
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Signal disambiguation is the ability to identify the source of each component signal from a single aggregated signal.
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This is a complicated problem as the different sources can affect each other, sometimes in a non-linear way.
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\subsection{Problem Statement}
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\begin{problem-statement}[Multi-Source Single-Measure]
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Given a discretized aggregated time series $t_a = t_1 \oplus t_2 \oplus \dots \oplus t_k$ and a set of patterns $P=\{(P_1\times\dots\times P_n)\}$, identify an injective mapping $m_{MSSM}:\mathbb{N}\longrightarrow P$ such that every sample $t_a[i]$ maps to a pattern set in $P$ with the condition that the sample matches an occurrence of the pattern in $t_a$.
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\end{problem-statement}
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The time series $t_a$ is a discretized, mono-variate, real-valued time series.
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The set of patterns $P$ is the cartesian product of the sets of patterns for each source $P_i$.
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Thus, each element of $P$ is a set of $n$ patterns, each associated with one source.
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Each set $P_i$ contain any number of pattern and the unknown $\chi$ pattern.
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The unknown pattern is not added to the set $P$ as the set of all $\chi$ is already present and bears the same meaning.
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The operator $\oplus$ is the aggregation function, generally the summation or caped summation.
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In some applications, the associativity of the $\oplus$ operator can be discarded as the aggregation is performed at the physical level, instantly across all sources $t_i$.
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\begin{figure}
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\centering
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\includegraphics[width=\textwidth]{images/mssm_illustration}
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\caption{Illustration of the MSSM setup.}
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\label{fig:mssm_illustration}
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\end{figure}
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\subsection{Applications}
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The successful design of a Multi-source Single-Measure monitoring system finds its best application in an industrial setting.
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Any industry that relies on many simple embedded systems to reliably perform a task can benefit from a monitoring system that is minimally disruptive to install.
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For example, an assembly line can leverage hundreds of conveyor belt drivers, robotic arms, or quality assessment points.
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Each type of system is simple in its design and task.
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However, adding a designated power monitoring measurement device to each individual system is costly, maintenance-heavy, and it multiplies the potential points of failure.
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Capturing the power consumption of these machines at a single point is an efficient way to minimize the implementation footprint while maintaining a reliable physics-based monitoring solution.
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PhD/research_proposal/glossaries.tex
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% List of Abbreviations (abbreviations type is built in to the glossaries-extra package)
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\newacronym{ids}{IDS}{Intrusion Detection System}
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\newacronym{cpu}{CPU}{Central Processing Unit}
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\newacronym{hids}{HIDS}{Host-based Intrusion Detection System}
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\newacronym{nids}{NIDS}{Network-based Intrusion Detection System}
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\newacronym{rtu}{RTU}{Remote Terminal Unit}
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\newacronym{psu}{PSU}{Power Supply Unit}
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\newacronym{os}{OS}{Operating System}
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\newacronym{wap}{WAP}{Wireless Access Point}
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\newacronym{eet}{EET}{Electromechanical Emission Tripwire}
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\newacronym{esg}{ESG}{Embedded Software Group}
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\newacronym{ssh}{SSH}{Secure Shell}
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\newacronym{apt}{APT}{Advanced Persistent Threat}
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\newacronym{mac}{MAC}{Media Access Controle}
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\newacronym{knn}{K-NN}{K Nearest Neighbors}
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\newacronym{svm}{SVM}{Support Vector Machine}
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\newacronym{rfc}{RFC}{Random Forest Classifier}
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\newacronym{adc}{ADC}{Analog Digital Converted}
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\newacronym{sbc}{SBC}{Single Board Computer}
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\newacronym{pc}{PC}{Personal Computer}
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\newacronym{bpv}{BPV}{Boot Process Verifier}
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\newacronym{bios}{BIOS}{Basic Input Output System}
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\newacronym{sds}{SDS}{State Detection and Segmentation}
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\newacronym{dsd}{DSD}{Device State Detector}
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\newacronym{ml}{ML}{Machine Learning}
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\newacronym{em}{EM}{Electro-Magnetic}
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\newacronym{mfcc}{MFCC}{Mel-Frequency Cepstrum Coefficients}
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\newacronym{iqr}{IQR}{Inter-Quartile Range}
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\newacronym{scs}{SCS}{Safety-Critical Systems}
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\newacronym{mcm}{MCM}{Machine Condition Monitoring}
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\newacronym{cnn}{CNN}{Concolutional Neural Network}
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\newacronym{mac}{MAC}{Media Access Control}
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PhD/research_proposal/images/SMMM_illustration.svg
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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
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107
PhD/research_proposal/introduction.tex
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|
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|
\chapter{Introduction to Physics-Based Security}
|
||||||
|
|
||||||
|
\section{Context}
|
||||||
|
\gls{scs} are those whose failure to perform their task could result in significant safety risks for humans or the environment.
|
||||||
|
These systems are present in many aspects of our daily life from transportation (ABS, airbags, traffic lights), or energy production (nuclear controle systems), to medicine (ventilation systems, radiation machines) and many others.
|
||||||
|
\gls{scs} are now more and more computer-based to enable features such as remote control or lower cost maintenance.
|
||||||
|
These systems are also increasingly connected to the internet to allow for offsite monitoring or data collection.
|
||||||
|
This digitalization of \gls{scs} also brings the undesirable aspects of connected computers.
|
||||||
|
The more connection and interraction types are available to a computer system, the greater is the risk of an attack using one of these connection to be discovered.
|
||||||
|
This sum of potential attack points, called attack surface, should typically be as reduced as possible, especially for \gls{scs} that require high reliability and availability.
|
||||||
|
Increasing the capabilities and connectivity of \gls{scs} enable large scale attacks that would be infeasible before.
|
||||||
|
For example, if all the water treatment plants in Canada are equipped with a data collection mechanisme exposed to the internet for centralized analysis, then an atacker could leverage this mechanism to take over all these system and put the whole country at risk.
|
||||||
|
|
||||||
|
A wide variety range of solutions are available to protect computer systems in general.
|
||||||
|
Among them, \gls{ids} aim at detecting security policies violations or suspicious activities from or among computers.
|
||||||
|
This detection is often enabled by the collection and analysis of data related to the machine's activity.
|
||||||
|
If the ressources considered are local to the machine (e.g. CPU load, RAM data, disks read/write speed), then the detection system is called \gls{hids}.
|
||||||
|
\gls{hids} have access to relevant local data\cn but they require to install a software on the machine (either for collection only or for local analysis).
|
||||||
|
This represent a potential flaw as the host machine may not be trusted and can be compromised, allowing the attacker to deploy stealth attacks \cite{10.1145/586110.586145}.
|
||||||
|
Moreover, an \gls{hids} can lack the broader vision required to detect intrusions distributed over a network of machines\cn.
|
||||||
|
The other main class of \gls{ids} aims at solving these issues.
|
||||||
|
\gls{nids} \cite{vigna1999netstat, bivens2002network} consider the communication between machines in a network to detect intrusions.
|
||||||
|
This solution does not require installing individual software on each machines and can detect network-level intrusions \cn.
|
||||||
|
However, \gls{nids} present their own concerns.
|
||||||
|
First, machine-specific attacks can remain undetected as only network information are accessible.
|
||||||
|
Then, they require the installation of dedicated equipment to collect network traffic.
|
||||||
|
Finally, modern traffic encryption practices will limit the \gls{nids} the sender-receiver pattern analysis unless traffic flows unencrypted, which can raises privacy issues.
|
||||||
|
It appears that the current \gls{ids} scene present a tradeoff between granularity of detection and isolation from the protected machine.
|
||||||
|
What about the case of protecting a machine against a local intrusion without the possibility to install additional software?
|
||||||
|
This use case can seem a niche one but it is a reality for many purpose-built embedded systems with minimal \gls{os}.
|
||||||
|
Systems like network switchs, \gls{rtu}, \gls{wap} rarely allow the installation of any software and yet are of critical importance.
|
||||||
|
In these cases, neither local ressources usage or network information can be leveraged for local attacks detection.
|
||||||
|
Moreover, any industry that rely on \gls{scs} have strict regulations (e.g. DO-178C for aerospace systems in Canada, ISO 26262 for automotive system, ISO 16142 for medical devices) that guarantee the safety of every equipment.
|
||||||
|
In this context, modifying an existing system to add intrusion detection capabilities is expensive as it requires the re-validation of the whole system.
|
||||||
|
An external solution relying on side-channel anaylysis is easier to get certified as it does not directly interact with the \gls{scs}
|
||||||
|
|
||||||
|
Another under-exploited source of information for embedded systems activity are the side-channels.
|
||||||
|
The side-channels are all the physical emissions that a machine involuntarely generates.
|
||||||
|
For example, the sound of a fan, the temperature of a CPU, or the power consumption of a \gls{psu} are all common side-channels \cn.
|
||||||
|
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=\linewidth]{images/side_channel}
|
||||||
|
\caption{Main side channels from a typical embedded systems.}
|
||||||
|
\label{fig:side_channel}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
Historically, side channels have been leveraged for attacks.\agd{rewrite this to not spoil the related work section but still present the context}
|
||||||
|
Eventhough the main use of side-channel analysis is to attack a system, the core idea is to retrieve information correlated with the system's activity.
|
||||||
|
With enougth knowledge of the normal behavior of a machine, an algorithm should be able to use the side-channel signature of a machine to assess its correct behavior.
|
||||||
|
This idea is called physics-based security and is the core principle of this research work.
|
||||||
|
|
||||||
|
|
||||||
|
\section{Proposal Organization}
|
||||||
|
This proposal is organized as follow: Section \ref{sec:related-work} presents an overview of the related work, Chapter \ref{chap:pastwork} presents the preliminary work conducted until now, Chapter \ref{chap:futurwork} presents the main problems I want to address during my research, and finally Chapter \ref{chap:timetable} draws a proposed timeline for the completion of the planned work.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\section{Related Work}\label{sec:related-work}
|
||||||
|
The idea of side-channel based IDS traces back to the seminal work in side-channel analysis by Paul C. Kocher.
|
||||||
|
He introduced Differential Power Analysis to find secret keys used by cryptographic protocols in tamper resistant devices~\cite{kocher1999differential}.
|
||||||
|
This led to a field of research focussing on side-channel analysis that has been ever growing. Power analysis is the most common and widely studied side-channel analysis technique~\cite{brier2004correlation,mangard2008power}. %new citations%
|
||||||
|
Cagalj et al.~\cite{vcagalj2014timing} show a successful passive side-channel timing attack on U.S. patent Mod 10 method and Hopper-Blum (HB) protocol.
|
||||||
|
Quisquater et al.~\cite{quisquater2002automatic} present an approach to identify executed instructions with the use of self-organizing maps, power analysis and analysis of electromagnetic traces. %new citations%
|
||||||
|
Zhai et al.~\cite{zhai2015method} propose a self-organizing maps approach that uses features extracted from an embedded processor to detect abnormal behavior in embedded devices.
|
||||||
|
Eisenbarth et al.~\cite{eisenbarth2010building} propose a methodology for recovering the instruction flow of microcontrollers using its power consumption.
|
||||||
|
Goldack et al.~\cite{goldack2008side} propose a solution to identify individual instructions on a PIC microcontroller through mapping each instruction type to a power consumption template.
|
||||||
|
Side-channel signatures can provide information about the integrity of a system.
|
||||||
|
Different teams at Georgia Tech University leveraged power and electromagnetic backscattering \cite{8701559, jorgensen2022efficient} to detect hardware trojans and counterfeit integrated circuit.
|
||||||
|
Due to its non-intrusive and architectur-agnostic nature, power fingerprinting has a wide range of applications from energy production systems \cite{6378346}, Software Defined Radio compliance assesments \cite{5379826}.
|
||||||
|
However, the attack focussed side-channel analysis can offer non-intrusive run-time monitoring, as well. \\
|
||||||
|
\indent
|
||||||
|
Literature shows promising work in assessing integrity through cache monitoring~\cite{7163050} and power monitoring~\cite{10.1145/2976749.2978299}.
|
||||||
|
Works by Moreno et al. offer two building blocks for this work.
|
||||||
|
In~\cite{moreno2013non}, the team proposes a solution for non-intrusive debugging and program tracing using side-channel analysis.
|
||||||
|
In this work, they use the power consumption of a given embedded system to identify the code block the embedded system was executing at the time.
|
||||||
|
The team builds on their previous technique and presents a new one~\cite{Moreno2018} using the power consumption of embedded systems for non-intrusive online run-time monitoring through anomaly detection.
|
||||||
|
They use a signals and systems analysis approach to identify anomalies using the power consumption of a system and show case this by identifying buffer overflow attacks on their system.
|
||||||
|
Msgna et al.~\cite{msgna2014verifying} propose a technique for using the instruction-level power consumption of a system to verify the integrity of the software components of a system with no prior knowledge of the software code.
|
||||||
|
In~\cite{kur2009improving}, Kur et al. perform power analysis of smart cards based on the JavaCard platform helps identify vulnerable operations, obtain bytecode instruction information, and also proposes a framework to replace vulnerable operations with safe alternatives.\\
|
||||||
|
\indent
|
||||||
|
In more recent literature, there is a trend towards the use of \gls{ml} for side-channel analysis to enhance the security of systems.
|
||||||
|
Michele Giovanni Calvi~\cite{calvi2019runtime} offers a solution for run time monitoring of an entire cyberphysical system treated as a black box.
|
||||||
|
They collect data from a self-driving car during operations such as steering and acceleration.
|
||||||
|
Using this data, they train an Long Short Term Memory~\cite{hochreiter1997long} deep learning model and use it to verify the safety of the vehicle. %new citations%
|
||||||
|
Zhengbing et al. \cite{4488501} suggest the use of forensic techniques for profiling user behaviour to detect intrusions and propose an intelligent lightweight \gls{ids}. Hanilçi et al.~\cite{hanilci2011recognition} use recorded speech from a cell phone to ascertain the cell phone brand and model through using vector quantization and \gls{svm} models on the \gls{mfcc} of the audio.
|
||||||
|
In~\cite{khan2019malware} Khan et al. propose a technique to identify malware in critical embedded and cyberphysical systems using \gls{em} side channel signals.
|
||||||
|
Their technique uses deep learning on EM emanation to model the behavior of an uncompromised system.
|
||||||
|
The system flags an activity as anomalous when the emanations differ from the normal ones used to train the neural network.
|
||||||
|
Sehatbakhsh et al.~\cite{sehatbakhsh2019remote} also use EM emanations and detect malware code injection into a known application without any prior knowledge of the malware signature.
|
||||||
|
They use HDBSCAN clustering method to identify anomalous behavior exhibited by the malicious code.
|
||||||
|
Yilmaz et al.~\cite{yilmaz2019detecting} implement K-Nearest Neighbors clustering methods along with PCA dimensionality reduction method to model EM emanations from a phone with the different operational status of front/rear camera.
|
||||||
|
Using the ML methods, they can determine the state of cellphone cameras.
|
||||||
|
|
||||||
|
A mechanical equivalent of physics-based security is \gls{mcm} that aims at monitoring the evolution of key parameters of a machine for health assessment.
|
||||||
|
This topic is not restricted to ditecting attackers activity and can inform about the health of the machine over timeto enable timely maintenance.
|
||||||
|
Different technics are deployed based on the machine type and the specific metrics of interest.
|
||||||
|
Machining equipment is often monitored with side-channel measurment such as vibration \cite{PENG2004199,4084702,HOU2021107451} sound \cite{sound_mcm}, temperature \cite{22438} or chemical analysis \cite{tavner1987condition}.
|
||||||
|
These technics focuses on mechanical machines with high reliability requirements and leverage side-channel information to reduce intrusivity.
|
||||||
|
|
||||||
|
On a larger scale, power consumption information for a whole house -- or even a whole building -- provides information about the activity of each appliance.
|
||||||
|
Monitoring, or prediction applications can leverage this information without the need for a measurment system on each endpoint.
|
||||||
|
This idea of non-intrusive load monitoring was first proposed by Hart in 1992 \cite{hart1992nonintrusive}.
|
||||||
|
The interests and challenges posed by the problem yielded different proposed solutions such as \gls{cnn} \cite{moradzadeh2021practical}, soft computing \cite{puente2020non}, or guassian models fitting on electromagnetic signatures \cite{10.1145/1864349.1864375}.
|
||||||
|
The concepts of signal disambiguation and individual consumption retrieval are transposable from a house omposed of appliances to an embedded system composed of devices.
|
||||||
|
|
||||||
|
|
||||||
352
PhD/research_proposal/pastwork.tex
Normal file
352
PhD/research_proposal/pastwork.tex
Normal file
|
|
@ -0,0 +1,352 @@
|
||||||
|
\chapter{Exploratory Work on Physics-Based Security}\label{chap:pastwork}
|
||||||
|
|
||||||
|
The \gls{esg} has a history of power side-channel analysis.
|
||||||
|
In 2017, the \gls{eet} project started with the aim to explore the intrusion detection capabilities of side-channel analysis.
|
||||||
|
A series of exploratory work on the topic of physics-based defense followed, each ilustrating a different capability
|
||||||
|
|
||||||
|
\section{Electromechanical Emission Tripwire}
|
||||||
|
The \gls{eet} project marked the start of the physics-based security at the ESG lab.
|
||||||
|
The project aims to evaluate the capabilities of physics-based security and provide a proof-of-concept.
|
||||||
|
The initial target was a network switch.
|
||||||
|
Network switches are a core component of any data center.
|
||||||
|
As powerful as computers can be, if they are not inter-connected, their computing power remains useless.
|
||||||
|
Communication becomes as essential as individual computing capabilities in a data center with hundreds of machines.
|
||||||
|
The failure of a network switch can have devastating consequences for data center operations.
|
||||||
|
Every minute of downtime costs the data center and its clients a fortune and must be prevented.
|
||||||
|
\gls{hids} are often not a perfect solution for network switches.
|
||||||
|
Their \gls{os} dont support additional software installation and may not propose built-it \gls{ids} capabilities.
|
||||||
|
When they do, the security solutions may be weak or rapidly out of date and fail to protect against attacks such as firmware modification~\cite{cisco_trust,thomson_2019} and bypassing secure boot-up~\cite{Cui2013WhenFM, hau_2015}.
|
||||||
|
They also fail to offer effective run-time monitoring through auditing and verifying log entries~\cite{koch2010security}.
|
||||||
|
For these reasons, network switches are prime candidates for side-channel security.
|
||||||
|
The installation of a side-channel monitoring system is often minimally invasive and can even be performed without downtime if the machine supports redundant power supplies.
|
||||||
|
The aim of the project was to leverage side-channel analysis to detect anomalous activities that can be related to attacks on a network switch.
|
||||||
|
The goal is not to create a complete \gls{ids} suite from physics-based security but to offer a complementary detection mechanism for the cases where traditional \gls{ids} are failing.
|
||||||
|
|
||||||
|
|
||||||
|
\subsection{Attack Scenario}
|
||||||
|
The attack surface on a network switch is large.
|
||||||
|
Every manageable switch has a management system that enable changing the parameters of the machine.
|
||||||
|
This management is typically accessible remotly via \gls{ssh}, telnet, HTTP or locally with a serial connection.
|
||||||
|
At least one of these interface shoud be available and they are typically protected with a username/password pair -- although certificate or key authentication may be available for modern interfaces like \gls{ssh}.
|
||||||
|
On top of these intended interface, a network switch is also at risk of attacks from the connected clients.
|
||||||
|
A malicious client connected to the switch can run \gls{MAC} flooding attack or VLAN hopping attack.
|
||||||
|
An attacker that gets physical access to the machine can also tamper with the firmware (upgrading/downgrading the firmware, uploading malicious firmware) or the hardware configuration of the machine.
|
||||||
|
|
||||||
|
We considered the following intrusions: a remote connection via \gls{ssh}, a firmware change, and a hardware change.
|
||||||
|
The remote connection via \gls{ssh} can be legitimate and does not always implies an intrusion.
|
||||||
|
However, this operation can be the first step of a more complex attack.
|
||||||
|
The network switch logs the connections for later forensics, but there is no mechanism to act on a remote connection on the default \gls{os}.
|
||||||
|
The capability of detecting a remote connection independently of the \gls{os} is valuable in a security pipeline.
|
||||||
|
Moreover, an attacker that gains access to the machine could wipe logs to cover their tracks.
|
||||||
|
With the detection mechanism isolated from the target machine, the attacker cannot bypass the detection.
|
||||||
|
A firmware change can also be a legitimate operation.
|
||||||
|
Updating the firmware is now a common capability on many embedded systems.
|
||||||
|
However, if the firmware change was not allowed by the system administrator, then it represents a threat.
|
||||||
|
Downgrading the firmware can re-open older security flaws that have since been documented.
|
||||||
|
Upgrading the firmware without approval can cause disruptions in the machine's operation.
|
||||||
|
Loading a modified version of the firmware can also enable an attacker to forge the firmware version and remain undetected from remote security or monitoring solutions.
|
||||||
|
Finally, a hardware change is also a security threat.
|
||||||
|
The machine that we considered for the experiment \agd{cite machine model} allows for the installation of additional port modules.
|
||||||
|
Each module expands the port capacity of the machine.
|
||||||
|
Modules can be \textit{hot-plugged} and will apply the default configuration of the machine.
|
||||||
|
Installing a new blade on a machine with a poor default configuration allows an attacker to set up various attacks.
|
||||||
|
For example, if the default configuration does not limit the number of \gls{mac} addresses, an \gls{mac} flooding attack can be performed to access restricted traffic.
|
||||||
|
This last scenario requires physical access to the machine.
|
||||||
|
|
||||||
|
|
||||||
|
\subsection{Host Independence}
|
||||||
|
One important aspect of the \gls{eet} technology is the independence between the host and the detection machine.
|
||||||
|
In a similar way to a \gls{nids}, the detection system is remote.
|
||||||
|
An attacker with access to the host does not have access to the detection system, which is important for the reliability of the results.
|
||||||
|
In the case of a \gls{hids}, the data are collected by a software on the host.
|
||||||
|
Whether these data are analyzed locally of sent to a remote machine makes no difference, as a compromised machine cannot be trusted to send genuine measurements.
|
||||||
|
An attacker with access to the machine can tamper with the measurement process to report nominal values and stay under the radar.
|
||||||
|
This problem is addressed by the \gls{eet} system as the power consumption of a software running on a machine cannot be faked and is difficult to hide.
|
||||||
|
The \gls{eet} system attempts to close the gap between host-\gls{ids} independence and access to relevant information about the machine's activities.
|
||||||
|
|
||||||
|
\subsection{Side-Channels}
|
||||||
|
Two side-channel were initially considered: electrical power consumption and ultrasound emissions.
|
||||||
|
The ultrasound emissions were quickly discarded.
|
||||||
|
When working with sound, the placement of the microphone is important and should be consistent.
|
||||||
|
This is a problem for the deployment of this technology to a variety of machines, as finding the best position of the microphone is complicated.
|
||||||
|
Moreover, the ultrasound measurements did not show the same level of detail as the power consumption.
|
||||||
|
Power consumption is a popular side-channel for many reasons:
|
||||||
|
it is easy to capture reliably with low-cost equipment,
|
||||||
|
the placement of the capture device has little impact on the results,
|
||||||
|
adding a capture device is often easy as it can be plugged in series with the main power cable of the machine,
|
||||||
|
it provides a good level of detail about the machine's activity.
|
||||||
|
We measured the power consumption in the form of power traces (time series of power measurements).
|
||||||
|
The capture device was a shunt resistor placed in series with the main power cable that generated a voltage drop proportional to the current (see figure \ref{fig:overview-eet1}).
|
||||||
|
We measured this voltage drop at a high frequency from 10-kilo samples per second (10KSPS) to one million samples per second (1MSPS).
|
||||||
|
|
||||||
|
\begin{figure}
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{images/overview_eet1.pdf}
|
||||||
|
\caption{Overview of the EET setup}
|
||||||
|
\label{fig:overview-eet1}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
\subsection{Results}
|
||||||
|
The detection of remote connection, firmware changes, and hardware changes were all successful.
|
||||||
|
More specifically, firmware change detection showed the most promising results.
|
||||||
|
The power consumption during the boot process is more stable and less noisy than during runtime.
|
||||||
|
Thanks to this consistency, changes between two firmware versions (see Figure \ref{fig:eet1_firmware}) are easy to detect with simple methods like \gls{knn}, \gls{svm} and \gls{rfc}.
|
||||||
|
All these methods yield good results for the detection of abnormal firmware.
|
||||||
|
|
||||||
|
\begin{figure}
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.8\textwidth]{images/eet1_firmware.pdf}
|
||||||
|
\caption{Boot-up sequences for two different firmware versions}
|
||||||
|
\label{fig:eet1_firmware}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
\newpage
|
||||||
|
\section{xPSU}\label{sec:xpsu}
|
||||||
|
The xPSU project continues the exploratory work started with the \gls{eet} project.
|
||||||
|
One important observation of the \gls{eet} project was that the global power consumption could be too noisy to extract all the relevant information is some cases.
|
||||||
|
One solution to this issue is to measure the power consumption at a lower level on specific components of interest.
|
||||||
|
The xPSU project aims at placing a power consumption probe and pre-processing system inside a regular \gls{pc}'s \gls{psu}.
|
||||||
|
The \gls{psu} is a prime location for monitoring power as it is responsible for generating the different power sources for the components of the \gls{pc}.
|
||||||
|
Integrating the measurement device in a \gls{psu} enables a \textit{drop-in} installation of the monitoring system in most \gls{pc}.
|
||||||
|
The capture mechanism consisted of a shunt resistor for generating the voltage drop, an \gls{adc} for measuring the value, and an \gls{sbc} for compiling and processing or sending the measurements.
|
||||||
|
The measurement and analysis did not require any communication with the host device to ensure independence.
|
||||||
|
The xPSU was an early proof of concept, and all the components could not fit in the \gls{psu}.
|
||||||
|
The fan of the \gls{psu} was moved outside of the enclosure, and the form factor of the \gls{psu} was modified.
|
||||||
|
As a result, the xPSU was not a perfect \textit{drop-in} replacement of a regular power supply, but the final form factor was encouraging.
|
||||||
|
With a better design of the capture system and a more appropriate choice of components (the raspberry pi is too large and powerful for the task), a more compact form factor could be achieved.
|
||||||
|
|
||||||
|
\begin{figure}
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.8\textwidth]{images/xpsu_illustration}
|
||||||
|
\caption{The xPSU focuses on a more granular measure of each components.}
|
||||||
|
\label{fig:xpsu}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
\subsection{Results}
|
||||||
|
We evaluated the performances of the xPSU on the task of detecting changes in hard drive firmware.
|
||||||
|
We placed the shunt resistor on the 5V cable of the Molex connector.
|
||||||
|
Although it is not an ordinary operation, it is possible to update the firmware of a hard drive.
|
||||||
|
Updates enable attackers to modify the firmware in the same way presented in the \gls{eet} project previously.
|
||||||
|
We selected drives with a pending firmware update for the experiment and measured their boot power trace before and after an update.
|
||||||
|
We also measured the trace of multiple drives of the same model and capacity to evaluate the detection of a drive replacement.
|
||||||
|
The results were satisfactory and illustrated the possibility of detecting a firmware change or a drive replacement from the boot power consumption of the drive captured from within the \gls{psu}.
|
||||||
|
|
||||||
|
\newpage
|
||||||
|
\section{Boot Process Verifier}\label{sec:bpv}
|
||||||
|
The good results of the \gls{eet} and xPSU projects paved the way for the development of a robust and versatile solution for verifying the boot process of a machine.
|
||||||
|
From the \gls{eet} project, we learned that modelling the expected trace (based on a number of known good boot traces) enabled the detection of anomalous firmware.
|
||||||
|
From the xPSU project, we learned that most embedded systems requiring a firmware would exhibit a firmware signature in the power consumption.
|
||||||
|
The base idea of the \gls{bpv} is to leverage a small number of known good firmware traces to build a model of normal boot power consumption.
|
||||||
|
With the model, a threshold is automatically computed to describe the acceptable range within which a new boot trace should fall to be considered normal.
|
||||||
|
If a new boot trace falls outside of the range, then it is abnormal, and an alert is raised.
|
||||||
|
The \gls{bpv} is not a tool for finding the root cause of an anomaly.
|
||||||
|
A root cause analysis can be applied later, but the \gls{bpv} is only responsible for detecting an anomaly.
|
||||||
|
The anomaly can result from malicious firmware, firmware upgrade/downgrade, or a change in firmware settings.
|
||||||
|
The \gls{eet} project also illustrated the potential of the simpler distance-based models.
|
||||||
|
A distance-based model was adopted for the \gls{bpv} to keep the maximum explainability of the model decision.
|
||||||
|
The \gls{bpv} is an approach to the following problem statement.
|
||||||
|
|
||||||
|
\begin{problem-statement}[Boot Process Verification]
|
||||||
|
Given a set of known-valid time series sample $S=\{s_1,\dots, s_n\}$ and a new unlabeled time series $t$, assign to $t$ the label \textit{valid} or \textit{anomalous} with the condition that the \textit{valid} label should only be assigned to new traces originating from the same distribution as the training samples from $S$.
|
||||||
|
\end{problem-statement}
|
||||||
|
|
||||||
|
The sample in $S$ and the unlabeled input $t$ are all discretized, real-valued time series of the same length.
|
||||||
|
The training samples $S$ all belong to the \textit{valid} class.
|
||||||
|
No example of the \textit{anomalous} class is accessible to the algorithm.
|
||||||
|
All samples in $S$ originate from the same distribution as they are different occurrences of boot sequences from the same machine with the same firmware and configuration.
|
||||||
|
|
||||||
|
The proposed solution was a distance-based detector with a threshold based on the \gls{iqr}.
|
||||||
|
The distance between two time series of same length is defined as the Euclidean distance and computed as $d(a,b)^2 = \sum_{i=0}^N(a[i]-b[i])^2$.
|
||||||
|
The \gls{iqr} is a measure of the dispersion of samples more robust to outliers than the variance.
|
||||||
|
It is based on the quartiles and defined as $IQR = Q_3 - Q_1$ with $Q_3$ the third quartile and $Q_1$ the first quartile.
|
||||||
|
This value is commonly used~\cite{han2011data} to detect outliers as a more robust alternative to the $3\sigma$ interval of a Gaussian distribution.
|
||||||
|
The training phase begins by computing the pointwise average trace.
|
||||||
|
Then, the \gls{iqr} of the distances from each trace to the average trace is computed.
|
||||||
|
Finally, the distance threshold takes the value $Q3 + 1.5\times IQR$.
|
||||||
|
The distance of each new trace to the reference average is computed and compared to the threshold in the detection phase.
|
||||||
|
If the distance is above the pre-computed threshold, the new trace is considered anomalous.
|
||||||
|
|
||||||
|
\subsection{Results}
|
||||||
|
We evaluated the \gls{bpv} on three occasions.
|
||||||
|
First, we assembled a panel of relevant devices, including switches, \gls{wap} and \gls{pc}.
|
||||||
|
The evaluations revealed that the \gls{bpv} performed better on simpler devices like switches and \gls{wap} compared to general-purpose computers.
|
||||||
|
This is mainly due to the reduced variability and noise in the traces captured from simpler devices that produce a more robust model.
|
||||||
|
This first study leads to the publication of a work-in-progress paper in the EMSOFT 2022 conference \cite{grisel2022work} that describes the design and capabilities of the \gls{bpv} in its first version.
|
||||||
|
Then, we performed a case study with an industry partner on \gls{rtu}.
|
||||||
|
The \gls{rtu} was composed of one low-complexity embedded system and one main general-purpose computer.
|
||||||
|
The computer's activity overtook most of the other information in the trace and made it more difficult to detect subtle variations.
|
||||||
|
However, the \gls{bpv} could still detect intrusions in the computer from the global trace.
|
||||||
|
For example, a user modifying some settings through the \gls{bios} or booting into a different \gls{os} was detected.
|
||||||
|
This case study revealed that some systems could have multiple valid modes of the boot sequence.
|
||||||
|
This discovery enabled us to rethink the model of the \gls{bpv} to allow such variations.
|
||||||
|
The final evaluation was performed on a drone.
|
||||||
|
A drone is a prime machine for the \gls{bpv} as its low complexity allows for consistent boot traces.
|
||||||
|
We successfully detected different firmware versions by leveraging the information from the two previous experiments.
|
||||||
|
Along the evaluations, the \gls{bpv} capabilities have been modified to adapt to specific cases and enable anomalous training samples, multi-model evaluations, and autonomous learning.
|
||||||
|
|
||||||
|
\newpage
|
||||||
|
\section{State Detection and Segmentation}
|
||||||
|
In Section~\ref{sec:bpv} we mentioned the use of distance metrics on boot power traces to evaluate their validity.
|
||||||
|
However, we never mentioned how these traces were detected, extracted, and synchronised.
|
||||||
|
This problem of pattern detection in a time series is more complexe than it seems and the boot sequence may not be known in advance, can take multiple form, and must still be detected if an anomalous boot radically changes the pattern.
|
||||||
|
The \gls{sds} algorithm was a first attempt at detected and extracting boot sequences for the \gls{bpv} to analyse.
|
||||||
|
The algorithm leverages two feature common to all (cold) boot sequences which are a sharp spike of power consumption and an average increase in the power consumption.
|
||||||
|
Two thresholds are manually set for the detection.
|
||||||
|
The first is the \textit{off\_threshold} that is the power consumption under which the machine is considered off.
|
||||||
|
The second is the \textit{boot\_time} which represent the time span of the boot procedure.
|
||||||
|
Each sample is considered and a set of rule is applied to decide on its state among \textit{OFF}, \textit{BOOT} and \textit{ON}.
|
||||||
|
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=\textwidth]{images/sds_illustration}
|
||||||
|
\caption{SDS detection mechanism using the y offset (\textit{off\_threshold}) and the x offset (\textit{bios\_time})}
|
||||||
|
\label{fig:sds_illustration}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\begin{algorithm}[H]
|
||||||
|
\caption{SDS}
|
||||||
|
\label{alg:sds}
|
||||||
|
\begin{algorithmic}[1]
|
||||||
|
\Require $trace$ the time serie of $N$ samples., $off\_threshold$, $bios\_time$.
|
||||||
|
|
||||||
|
\State $pstates \gets array(N-1)$
|
||||||
|
\State $states \gets array(N)$
|
||||||
|
\State $boot\_time \gets None$
|
||||||
|
|
||||||
|
\For{$i \in [0,\dots, N]$}
|
||||||
|
\State $s \gets trace[i].value$
|
||||||
|
\State $t \gets trace[i].time$
|
||||||
|
|
||||||
|
\If{$s < off\_threshold$}
|
||||||
|
\State $states[i] \gets OFF$
|
||||||
|
\Else
|
||||||
|
\If{$i=0$}
|
||||||
|
\State $states[i] \gets ON$
|
||||||
|
\ElsIf{$pstate[i-1] = OFF$}
|
||||||
|
\State $states[i] \gets BOOT$
|
||||||
|
\State $boot_time \gets t$
|
||||||
|
\ElsIf{$pstates[i-1] = ON$}
|
||||||
|
\State $states[i] \gets ON$
|
||||||
|
\Else
|
||||||
|
\If{$t - boot\_time < bios\_time$}
|
||||||
|
\State $states[i] \gets BOOT$
|
||||||
|
\Else
|
||||||
|
\State $states[i] \gets ON$
|
||||||
|
\EndIf
|
||||||
|
\EndIf
|
||||||
|
\EndIf
|
||||||
|
\State $pstate[i] \gets states[i]$
|
||||||
|
\EndFor
|
||||||
|
|
||||||
|
\end{algorithmic}
|
||||||
|
\end{algorithm}
|
||||||
|
|
||||||
|
|
||||||
|
This simple algorithm makes the \gls{sds} robust and reliable but also limited.
|
||||||
|
The \gls{sds} is an appropriate solution for states that exhibit a change in average consumption and with pre-defined duration.
|
||||||
|
The detection of consistent and synchronized bootup sequences fits perfectly in this use case.
|
||||||
|
This consistency and synchrony of the instances are essential for distance-based detectors which compare these instances.
|
||||||
|
However, for the states that cannot be described by a change in average consumption and duration, the \gls{sds} is incompetent.
|
||||||
|
For example, if a machine can perform two runtime operations that generate the same consumption pattern but with different frequencies, then the \gls{sds} cannot distinguish these two states reliably.
|
||||||
|
These limitations make the \gls{sds} a preliminary work, not a final solution.
|
||||||
|
It outlines that state detection is a complex problem and that the properties of the output need to be taken into account during the design.
|
||||||
|
If the desired output is only the information of the state occurrence, then the perfect consistency and synchronization of the extract are not required.
|
||||||
|
If the output is expected to be processed by a follow-up algorithm, and especially if it is distance-based, then the output needs to be much more consistent and synchronized.
|
||||||
|
These considerations reveal a tradeoff between training data and capabilities.
|
||||||
|
The \gls{sds} required no training data except for the two threshold values.
|
||||||
|
This is interesting from a deployment perspective, where machine data can be scarce.
|
||||||
|
It also impacts the detection capability as the \gls{sds} does not look for actual patterns but for single values.
|
||||||
|
|
||||||
|
\newpage
|
||||||
|
\section{Device State Detector}
|
||||||
|
The \gls{dsd} is the continuation of the \gls{sds}.
|
||||||
|
The algorithm's goal remains the same; detect the machine's state.
|
||||||
|
However, the detection process and the outputs are fundamentally different.
|
||||||
|
The \gls{sds} was built with robustness, ease of training and consistency.
|
||||||
|
The keywords for the \gls{dsd} would be versatility and range of application.
|
||||||
|
You may already guess \agd{familiar?} that the synchronization and consistency of the output will not be the main focus of the \gls{dsd}, and they will be replaced by a greater versatility of the state detection at the cost of more training data.
|
||||||
|
The \gls{dsd} fits in a family of problems that are similar but differ by the natur of the data leveraged.
|
||||||
|
Until now, we only took into accound the case of the power consumption of a single machine (or single source) captured at a single point (single measure).
|
||||||
|
Other variation of the same problem (multi sources, multi measures, ...) will studies in the next chapter.
|
||||||
|
The \gls{dsd} algorithm is an approach to the following problem statement.
|
||||||
|
|
||||||
|
\begin{problem-statement}[Single-Source Single-Measure]
|
||||||
|
Given a discretized time series $t$ and a set of patterns $P=\{\chi, P_1,\dots, P_n\}$, identify an injective mapping $m_{SSSM}:\mathbb{N}\longrightarrow P$ such that every sample $t[i]$
|
||||||
|
maps to a pattern in $P$ with the condition that the sample matches an occurence of the pattern in $t$.
|
||||||
|
\end{problem-statement}
|
||||||
|
|
||||||
|
The time serie $t: \mathbb{N} \longrightarrow \mathbb{R}$ is a discretized, mono-variate, real-valued time series.
|
||||||
|
The patterns $P_j \in P\setminus \chi$ are of the same type as $t$.
|
||||||
|
A sample $t[i]$ \textit{matches} a pattern $P_j \in P\setminus \chi$ if there exists a subsequence of $t$, the length of $P_j$, that include the sample, such that a similarity measure between this subsequence and $P_j$ is below a pre-defined threshold.
|
||||||
|
The pattern $\chi$ is the unknown pattern assigned to the samples in $t$ that dont match any of the $P_j$ patterns.
|
||||||
|
|
||||||
|
\begin{figure}
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{images/dsd_illustration}
|
||||||
|
\caption{Illustration of the DSD input and output.}
|
||||||
|
\label{fig:dsd_illustration}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
The core of the algorithm is the \gls{knn} classification.
|
||||||
|
This algorithm is a proven and robust way of labelling new samples based on their relative similarity to the training samples.
|
||||||
|
Although this is a good algorithm for various problems, its application to time series for pattern matching is not obvious.
|
||||||
|
For the rest of the explanation of the \gls{dsd} we will suppose that the training data consists of one time series per state.
|
||||||
|
These time series represent one occurrence of a state to detect.
|
||||||
|
One important detail is that each training sample can have a different length as the states are likely not all of the same duration.
|
||||||
|
The default way of applying a \gls{knn} classifier for detecting patterns in a long time series would be to iteratively consider slices of the trace corresponding to each length of the training sample.
|
||||||
|
Then the classifier would evaluate the distance of the slices to the training sample and normalize this distance by the length to generate comparable values.
|
||||||
|
The state of the closest training sample is assigned to each sample of the slice, and the next slice is considered without overlap.
|
||||||
|
The results for this method are sub-optimal.
|
||||||
|
The stride between each window is too large, and crucial patterns can be overlooked in the trace.
|
||||||
|
Moreover, the whole window will be assigned one label, which causes the edges of the states to be inaccurate.
|
||||||
|
|
||||||
|
The \gls{dsd} uses a better metric for evaluating the distance between a sample and each state.
|
||||||
|
For each sample and for each state, every window of the length of the state containing the sample is considered.
|
||||||
|
The first window contains the sample at the last position, and the last window contains the sample at the first position.\agd{add figure to explain that}
|
||||||
|
The algorithm computes the distance between each window and the state and normalizes it by the length of the state.
|
||||||
|
After all the distances are computed, we can assign to the sample the state that is the closest.
|
||||||
|
This method naturally segments the state space into areas where the borders represent a mid-point between two states.\agd{figure}
|
||||||
|
We refined the method by introducing a coefficient to shrink the capture areas of each state.
|
||||||
|
The emerging area corresponding to no state allows for the detection of unseen states.
|
||||||
|
This method retains the low complexity of a distance-based \gls{knn} algorithm while yielding better accuracy, especially around state transitions.
|
||||||
|
The \gls{dsd} was designed for one-shot classification, but the multi-shot version is naturally accessible by adding more training examples and going from a 1-NN to a K-NN.
|
||||||
|
Two metrics represent the performance of the \gls{dsd} and any other algorithm for the same problem.
|
||||||
|
First, the accuracy is computed as the number of correct labels over the total number of labels to predict.
|
||||||
|
This metric is common and gives an overview of the performances comparable with a random baseline.
|
||||||
|
However, the knowledge of the specific applications that the \gls{dsd} is designed for allow for the definition of a complementary metric.
|
||||||
|
The label of each sample makes the time series actionable.
|
||||||
|
Other algorithms down the processing pipeline can evaluate the sequence of states detected for a machine in order to decide on the integrity of the machine.
|
||||||
|
In this regard, a labeling error can have different impact depending on the location.
|
||||||
|
More specifically, a single error at the transition between two states would result in a slight timing error for the state transition detection.
|
||||||
|
However, a single error in the middle of a series of identical labels would result in the detection of a new incorrect state, potentially triggering actions down the line.
|
||||||
|
These two errors have the same impact on the accuracy.
|
||||||
|
This illustrates that the accuracy is not the complete picture.
|
||||||
|
To evaluate the state detection at a higher level, the levenshtein distance of the reduced labels is defined.
|
||||||
|
The reduced labels is the vector of labels with every sequence of identical labels represented as only one symbole.
|
||||||
|
The normalized state edit distance is defined as
|
||||||
|
\begin{equation}
|
||||||
|
nsed(truth,preds) = \dfrac{1}{max(reduced(truth),reduced(preds)}*Lev(reduced(truth),reduced(preds))
|
||||||
|
\end{equation}
|
||||||
|
with $Lev$ the Levenshtein distance.
|
||||||
|
This metric is complementary to the accuracy and will be computed for every evaluation of the the state detection algorithms.
|
||||||
|
|
||||||
|
|
||||||
|
\newpage
|
||||||
|
\section{Conclusion on Past Work}
|
||||||
|
The project of physics-based security at a global level is not trivial.
|
||||||
|
The main hurdle is the extraction of information with a dual constraint of only collecting unlabeled and partial information (the power consumption) and not having any control over the machine's activity (host independance).
|
||||||
|
However, these constraints are also the strengths of this approach.
|
||||||
|
The power consumption is a limited but reliable source of information as it is very difficult to forge.
|
||||||
|
It is up to the algorithm to extract as much information from it.
|
||||||
|
The independence is also important as it guarantees that an attacker cannot bypass the detection mechanism.
|
||||||
|
|
||||||
|
With these constraints in mind, the current results illustrate\agd{change word} a great potential.
|
||||||
|
The \gls{bpv} and \gls{dsd} algorithm tackle the problems of boot process integrity and runtime activity monitoring.
|
||||||
|
These two complementary aspects represent a large area of the attack surface of a typical embedded system.
|
||||||
|
The unique properties of host independence and unforgeability of the input data make the physics-based \gls{ids} a promising complement for any security suite.
|
||||||
|
|
||||||
|
More work is obviously required.
|
||||||
|
The main study\agd{change word} currently is to evaluate the performance of the \gls{dsd} to make it as versatile and reliable as possible.
|
||||||
|
From the xPSU project, we understood that a more granular measurement of the power consumption could be beneficial in detecting specific attacks and enabling root cause analysis instead of basic anomaly detection.
|
||||||
|
The continuation of the research work will focus on runtime monitoring and investigate the data measurement scales and their respective benefits for a better security solution \agd{change solution, marketing term}.
|
||||||
55
PhD/research_proposal/presentation.tex
Normal file
55
PhD/research_proposal/presentation.tex
Normal file
|
|
@ -0,0 +1,55 @@
|
||||||
|
\documentclass[aspectratio=169,10pt]{beamer}
|
||||||
|
\usetheme[progressbar=head,numbering=fraction,sectionpage=none]{metropolis}
|
||||||
|
|
||||||
|
\usepackage{graphicx}
|
||||||
|
\usepackage{ulem}
|
||||||
|
\usepackage{xcolor}
|
||||||
|
\usepackage[scale=2]{ccicons}
|
||||||
|
\usepackage{pgfplots}
|
||||||
|
\usepackage{booktabs}
|
||||||
|
\usepgfplotslibrary{dateplot}
|
||||||
|
\usepackage{hyperref}
|
||||||
|
\usepackage{multirow}
|
||||||
|
\usepackage{array}
|
||||||
|
\usepackage{xspace}
|
||||||
|
|
||||||
|
\title{PhD Research proposal: Physics Based Security}
|
||||||
|
\subtitle{}
|
||||||
|
\date{}
|
||||||
|
\author{Arthur Grisel-Davy}
|
||||||
|
\institute{University of Waterloo, Canada}
|
||||||
|
|
||||||
|
|
||||||
|
\begin{document}
|
||||||
|
|
||||||
|
\maketitle
|
||||||
|
|
||||||
|
\begin{frame}{Introduction}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}{Common IDS Solution}
|
||||||
|
% Figure from the EMSOFT presentation with the different side channels and our solution
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}{Side Channels}
|
||||||
|
% advantages of power side channel
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}{Prelimirary Work - EET1}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}{ Preliminary Work - xPSU}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}{Preliminary Work - SDS \& BPV}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
\begin{frame}{Main problem Statement}
|
||||||
|
% Problem statement for DSD
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\end{document}
|
||||||
|
|
||||||
250
PhD/research_proposal/proposal.tex
Normal file
250
PhD/research_proposal/proposal.tex
Normal file
|
|
@ -0,0 +1,250 @@
|
||||||
|
%======================================================================
|
||||||
|
% University of Waterloo Thesis Template for LaTeX
|
||||||
|
% Last Updated August 2022
|
||||||
|
% by IST Client Services,
|
||||||
|
% University of Waterloo, 200 University Ave. W., Waterloo, Ontario, Canada
|
||||||
|
% FOR ASSISTANCE, please send mail to helpdesk@uwaterloo.ca
|
||||||
|
|
||||||
|
% DISCLAIMER
|
||||||
|
% To the best of our knowledge, this template satisfies the current uWaterloo thesis requirements.
|
||||||
|
% However, it is your responsibility to assure that you have met all requirements of the University and your particular department.
|
||||||
|
|
||||||
|
% Many thanks for the feedback from many graduates who assisted the development of this template.
|
||||||
|
% Also note that there are explanatory comments and tips throughout this template.
|
||||||
|
%======================================================================
|
||||||
|
% Some important notes on using this template and making it your own...
|
||||||
|
|
||||||
|
% The University of Waterloo has required electronic thesis submission since October 2006.
|
||||||
|
% See the uWaterloo thesis regulations at
|
||||||
|
% https://uwaterloo.ca/graduate-studies/thesis.
|
||||||
|
% This thesis template is geared towards generating a PDF version optimized for viewing on an electronic display, including hyperlinks within the PDF.
|
||||||
|
|
||||||
|
% DON'T FORGET TO ADD YOUR OWN NAME AND TITLE in the "hyperref" package configuration below.
|
||||||
|
% Search for: PDFTITLE, PDFAUTHOR, PDFSUBJECT, and PDFKEYWORDS.
|
||||||
|
% THIS INFORMATION GETS EMBEDDED IN THE FINAL PDF DOCUMENT.
|
||||||
|
% You can view the information if you view properties of the PDF document.
|
||||||
|
|
||||||
|
% Many faculties/departments also require one or more printed copies.
|
||||||
|
% This template attempts to satisfy both types of output.
|
||||||
|
% See additional notes below.
|
||||||
|
% It is based on the standard "book" document class which provides all necessary sectioning structures and allows multi-part theses.
|
||||||
|
|
||||||
|
% If you are using this template in Overleaf (cloud-based collaboration service), then it is automatically processed and previewed for you as you edit.
|
||||||
|
|
||||||
|
% For people who prefer to install their own LaTeX distributions on their own computers, and process the source files manually, the following notes provide the sequence of tasks:
|
||||||
|
|
||||||
|
% E.g. to process a thesis called "mythesis.tex" based on this template, run:
|
||||||
|
|
||||||
|
% pdflatex mythesis -- first pass of the pdflatex processor
|
||||||
|
% bibtex mythesis -- generates bibliography from .bib data file(s)
|
||||||
|
% makeindex -- should be run only if an index is used
|
||||||
|
% pdflatex mythesis -- fixes numbering in cross-references, bibliographic references, glossaries, index, etc.
|
||||||
|
% pdflatex mythesis -- it takes a couple of passes to completely process all cross-references
|
||||||
|
|
||||||
|
% If you use the recommended LaTeX editor, Texmaker, you would open the mythesis.tex file, then click the PDFLaTeX button. Then run BibTeX (under the Tools menu).
|
||||||
|
% Then click the PDFLaTeX button two more times.
|
||||||
|
% If you have an index as well,you'll need to run MakeIndex from the Tools menu as well, before running pdflatex
|
||||||
|
% the last two times.
|
||||||
|
|
||||||
|
% N.B. The "pdftex" program allows graphics in the following formats to be included with the "\includegraphics" command: PNG, PDF, JPEG, TIFF
|
||||||
|
% Tip: Generate your figures and photos in the size you want them to appear in your thesis, rather than scaling them with \includegraphics options.
|
||||||
|
% Tip: Any drawings you do should be in scalable vector graphic formats: SVG, PNG, WMF, EPS and then converted to PNG or PDF, so they are scalable in the final PDF as well.
|
||||||
|
% Tip: Photographs should be cropped and compressed so as not to be too large.
|
||||||
|
|
||||||
|
% To create a PDF output that is optimized for double-sided printing:
|
||||||
|
% 1) comment-out the \documentclass statement in the preamble below, and un-comment the second \documentclass line.
|
||||||
|
% 2) change the value assigned below to the boolean variable "PrintVersion" from " false" to "true".
|
||||||
|
|
||||||
|
%======================================================================
|
||||||
|
% D O C U M E N T P R E A M B L E
|
||||||
|
% Specify the document class, default style attributes, and page dimensions, etc.
|
||||||
|
% For hyperlinked PDF, suitable for viewing on a computer, use this:
|
||||||
|
\documentclass[letterpaper,12pt,titlepage,oneside,final]{book}
|
||||||
|
|
||||||
|
% For PDF, suitable for double-sided printing, change the PrintVersion variable below to "true" and use this \documentclass line instead of the one above:
|
||||||
|
%\documentclass[letterpaper,12pt,titlepage,openright,twoside,final]{book}
|
||||||
|
|
||||||
|
% Some LaTeX commands I define for my own nomenclature.
|
||||||
|
% If you have to, it's easier to make changes to nomenclature once here than in a million places throughout your thesis!
|
||||||
|
\newcommand{\package}[1]{\textbf{#1}} % package names in bold text
|
||||||
|
\newcommand{\cmmd}[1]{\textbackslash\texttt{#1}} % command name in tt font
|
||||||
|
\newcommand{\href}[1]{#1} % does nothing, but defines the command so the print-optimized version will ignore \href tags (redefined by hyperref pkg).
|
||||||
|
%\newcommand{\texorpdfstring}[2]{#1} % does nothing, but defines the command
|
||||||
|
% Anything defined here may be redefined by packages added below...
|
||||||
|
|
||||||
|
% This package allows if-then-else control structures.
|
||||||
|
\usepackage{ifthen}
|
||||||
|
\newboolean{PrintVersion}
|
||||||
|
\setboolean{PrintVersion}{false}
|
||||||
|
% CHANGE THIS VALUE TO "true" as necessary, to improve printed results for hard copies by overriding some options of the hyperref package, called below.
|
||||||
|
|
||||||
|
%\usepackage{nomencl} % For a nomenclature (optional; available from ctan.org)
|
||||||
|
\usepackage{amsmath,amssymb,amstext} % Lots of math symbols and environments
|
||||||
|
\usepackage[pdftex]{graphicx} % For including graphics N.B. pdftex graphics driver
|
||||||
|
\usepackage{xcolor}
|
||||||
|
|
||||||
|
\usepackage{algorithm}
|
||||||
|
\usepackage{algpseudocode}
|
||||||
|
%\usepackage{algorithmic}
|
||||||
|
|
||||||
|
% Hyperlinks make it very easy to navigate an electronic document.
|
||||||
|
% In addition, this is where you should specify the thesis title and author as they appear in the properties of the PDF document.
|
||||||
|
% Use the "hyperref" package
|
||||||
|
% N.B. HYPERREF MUST BE THE LAST PACKAGE LOADED; ADD ADDITIONAL PKGS ABOVE
|
||||||
|
\usepackage[pdftex,pagebackref=false]{hyperref} % with basic options
|
||||||
|
%\usepackage[pdftex,pagebackref=true]{hyperref}
|
||||||
|
% N.B. pagebackref=true provides links back from the References to the body text. This can cause trouble for printing.
|
||||||
|
|
||||||
|
\newcommand{\mytitle}{Ph.D Research Proposal}
|
||||||
|
\newcommand{\myauthor}{Arthur Grisel-Davy}
|
||||||
|
\newcommand{\mytopic}{Physics-Based Cyber Security}
|
||||||
|
\newcommand{\myinstitution}{University of Waterloo}
|
||||||
|
|
||||||
|
\newtheorem{problem-statement}{Problem Statement}
|
||||||
|
|
||||||
|
\hypersetup{
|
||||||
|
plainpages=false, % needed if Roman numbers in frontpages
|
||||||
|
unicode=false, % non-Latin characters in Acrobat’s bookmarks
|
||||||
|
pdftoolbar=true, % show Acrobat’s toolbar?
|
||||||
|
pdfmenubar=true, % show Acrobat’s menu?
|
||||||
|
pdffitwindow=false, % window fit to page when opened
|
||||||
|
pdfstartview={FitH}, % fits the width of the page to the window
|
||||||
|
pdftitle={\mytitle}, % title: CHANGE THIS TEXT!
|
||||||
|
pdfauthor={\myauthor}, % author: CHANGE THIS TEXT! and uncomment this line
|
||||||
|
pdfsubject={\mytopic}, % subject: CHANGE THIS TEXT! and uncomment this line
|
||||||
|
% pdfkeywords={Power Analysis} {Sid} {key3}, % list of keywords, and uncomment this line if desired
|
||||||
|
pdfnewwindow=true, % links in new window
|
||||||
|
colorlinks=true, % false: boxed links; true: colored links
|
||||||
|
linkcolor=blue, % color of internal links
|
||||||
|
citecolor=green, % color of links to bibliography
|
||||||
|
filecolor=magenta, % color of file links
|
||||||
|
urlcolor=cyan % color of external links
|
||||||
|
}
|
||||||
|
\ifthenelse{\boolean{PrintVersion}}{ % for improved print quality, change some hyperref options
|
||||||
|
\hypersetup{ % override some previously defined hyperref options
|
||||||
|
% colorlinks,%
|
||||||
|
citecolor=black,%
|
||||||
|
filecolor=black,%
|
||||||
|
linkcolor=black,%
|
||||||
|
urlcolor=black}
|
||||||
|
}{} % end of ifthenelse (no else)
|
||||||
|
|
||||||
|
\usepackage{cite}
|
||||||
|
\usepackage[acronyms]{glossaries} % Exception to the rule of hyperref being the last add-on package
|
||||||
|
% If glossaries-extra is not in your LaTeX distribution, get it from CTAN (http://ctan.org/pkg/glossaries-extra),
|
||||||
|
% although it's supposed to be in both the TeX Live and MikTeX distributions. There are also documentation and
|
||||||
|
% installation instructions there.
|
||||||
|
\input{glossaries}
|
||||||
|
|
||||||
|
% Setting up the page margins...
|
||||||
|
% uWaterloo thesis requirements specify a minimum of 1 inch (72pt) margin at the
|
||||||
|
% top, bottom, and outside page edges and a 1.125 in. (81pt) gutter margin (on binding side).
|
||||||
|
% While this is not an issue for electronic viewing, a PDF may be printed, and so we have the same page layout for both printed and electronic versions, we leave the gutter margin in.
|
||||||
|
% Set margins to minimum permitted by uWaterloo thesis regulations:
|
||||||
|
\setlength{\marginparwidth}{0pt} % width of margin notes
|
||||||
|
% N.B. If margin\newcommand\comment[1]{\textbf{#1}}\input{myfile} notes are used, you must adjust \textwidth, \marginparwidth
|
||||||
|
% and \marginparsep so that the space left between the margin notes and page
|
||||||
|
% edge is less than 15 mm (0.6 in.)
|
||||||
|
\setlength{\marginparsep}{0pt} % width of space between body text and margin notes
|
||||||
|
\setlength{\evensidemargin}{0.125in} % Adds 1/8 in. to binding side of all
|
||||||
|
% even-numbered pages when the "twoside" printing option is selected
|
||||||
|
\setlength{\oddsidemargin}{0.125in} % Adds 1/8 in. to the left of all pages when "oneside" printing is selected, and to the left of all odd-numbered pages when "twoside" printing is selected
|
||||||
|
\setlength{\textwidth}{6.375in} % assuming US letter paper (8.5 in. x 11 in.) and side margins as above
|
||||||
|
\raggedbottom
|
||||||
|
|
||||||
|
% The following statement specifies the amount of space between paragraphs. Other reasonable specifications are \bigskipamount and \smallskipamount.
|
||||||
|
\setlength{\parskip}{\medskipamount}
|
||||||
|
|
||||||
|
% The following statement controls the line spacing.
|
||||||
|
% The default spacing corresponds to good typographic conventions and only slight changes (e.g., perhaps "1.2"), if any, should be made.
|
||||||
|
\renewcommand{\baselinestretch}{1} % this is the default line space setting
|
||||||
|
|
||||||
|
% By default, each chapter will start on a recto (right-hand side) page.
|
||||||
|
% We also force each section of the front pages to start on a recto page by inserting \cleardoublepage commands.
|
||||||
|
% In many cases, this will require that the verso (left-hand) page be blank, and while it should be counted, a page number should not be printed.
|
||||||
|
% The following statements ensure a page number is not printed on an otherwise blank verso page.
|
||||||
|
\let\origdoublepage\cleardoublepage
|
||||||
|
\newcommand{\clearemptydoublepage}{%
|
||||||
|
\clearpage{\pagestyle{empty}\origdoublepage}}
|
||||||
|
\let\cleardoublepage\clearemptydoublepage
|
||||||
|
|
||||||
|
% Define Glossary terms (This is properly done here, in the preamble and could also be \input{} from a separate file...)
|
||||||
|
|
||||||
|
% Custom command to print visual indications of comments
|
||||||
|
\newcommand\agd[1]{{\color{red}$\bigstar$}\footnote{agd: #1}}
|
||||||
|
\newcommand{\cn}{{\color{purple}[citation needed]}}
|
||||||
|
|
||||||
|
%======================================================================
|
||||||
|
% L O G I C A L D O C U M E N T
|
||||||
|
% The logical document contains the main content of your thesis.
|
||||||
|
% Being a large document, it is a good idea to divide your thesis into several files, each one containing one chapter or other significant chunk of content, so you can easily shuffle things around later if desired.
|
||||||
|
%======================================================================
|
||||||
|
\begin{document}
|
||||||
|
|
||||||
|
%----------------------------------------------------------------------
|
||||||
|
% FRONT MATERIAL
|
||||||
|
% title page,declaration, borrowers' page, abstract, acknowledgements,
|
||||||
|
% dedication, table of contents, list of tables, list of figures, nomenclature, etc.
|
||||||
|
%----------------------------------------------------------------------
|
||||||
|
\input{frontpages}
|
||||||
|
|
||||||
|
%----------------------------------------------------------------------
|
||||||
|
% MAIN BODY
|
||||||
|
% We suggest using a separate file for each chapter of your thesis.
|
||||||
|
% Start each chapter file with the \chapter command.
|
||||||
|
% Only use \documentclass or \begin{document} and \end{document} commands in this master document.
|
||||||
|
% Tip: Putting each sentence on a new line is a way to simplify later editing.
|
||||||
|
%----------------------------------------------------------------------
|
||||||
|
\input{introduction}
|
||||||
|
\input{pastwork}
|
||||||
|
\input{futurwork}
|
||||||
|
\input{timetable}
|
||||||
|
\input{conclusion}
|
||||||
|
%----------------------------------------------------------------------
|
||||||
|
% END MATERIAL
|
||||||
|
% Bibliography, Appendices, Index, etc.
|
||||||
|
%----------------------------------------------------------------------
|
||||||
|
|
||||||
|
% Bibliography
|
||||||
|
|
||||||
|
% The following statement selects the style to use for references.
|
||||||
|
% It controls the sort order of the entries in the bibliography and also the formatting for the in-text labels.
|
||||||
|
\bibliographystyle{plain}
|
||||||
|
% This specifies the location of the file containing the bibliographic information.
|
||||||
|
% It assumes you're using BibTeX to manage your references (if not, why not?).
|
||||||
|
\cleardoublepage % This is needed if the "book" document class is used, to place the anchor in the correct page, because the bibliography will start on its own page.
|
||||||
|
% Use \clearpage instead if the document class uses the "oneside" argument
|
||||||
|
\phantomsection % With hyperref package, enables hyperlinking from the table of contents to bibliography
|
||||||
|
% The following statement causes the title "References" to be used for the bibliography section:
|
||||||
|
\renewcommand*{\bibname}{References}
|
||||||
|
|
||||||
|
% Add the References to the Table of Contents
|
||||||
|
\addcontentsline{toc}{chapter}{\textbf{References}}
|
||||||
|
|
||||||
|
\bibliography{bibliography.bib}
|
||||||
|
% Tip: You can create multiple .bib files to organize your references.
|
||||||
|
% Just list them all in the \bibliogaphy command, separated by commas (no spaces).
|
||||||
|
|
||||||
|
% The following statement causes the specified references to be added to the bibliography even if they were not cited in the text.
|
||||||
|
% The asterisk is a wildcard that causes all entries in the bibliographic database to be included (optional).
|
||||||
|
%\nocite{*}
|
||||||
|
%----------------------------------------------------------------------
|
||||||
|
|
||||||
|
% Appendices
|
||||||
|
|
||||||
|
% The \appendix statement indicates the beginning of the appendices.
|
||||||
|
\appendix
|
||||||
|
% Add an un-numbered title page before the appendices and a line in the Table of Contents
|
||||||
|
\chapter*{APPENDICES}
|
||||||
|
\addcontentsline{toc}{chapter}{APPENDICES}
|
||||||
|
% Appendices are just more chapters, with different labeling (letters instead of numbers).
|
||||||
|
|
||||||
|
% GLOSSARIES (Lists of definitions, abbreviations, symbols, etc. provided by the glossaries-extra package)
|
||||||
|
% -----------------------------
|
||||||
|
%\printglossaries
|
||||||
|
%\cleardoublepage
|
||||||
|
%\phantomsection % allows hyperref to link to the correct page
|
||||||
|
|
||||||
|
%----------------------------------------------------------------------
|
||||||
|
\end{document} % end of logical document
|
||||||
|
|
||||||
32
PhD/research_proposal/timetable.tex
Normal file
32
PhD/research_proposal/timetable.tex
Normal file
|
|
@ -0,0 +1,32 @@
|
||||||
|
\chapter{Timetable}\label{chap:timetable}
|
||||||
|
The planned work is segmented into three main parts: finishing the \gls{dsd}, building the data acquisition system and building to algorithm for the single-source multi-measure system, and setting up an experiment for the multi-source single-measure system.
|
||||||
|
Each of these three parts has its own specificities and challenges that call for careful planning.
|
||||||
|
|
||||||
|
\section{Winter 2023}
|
||||||
|
The main focus for this term is the writing of the \gls{dsd} paper.
|
||||||
|
The algorithm has now reached a satisfactory state with a good range of detection and useful precision.
|
||||||
|
However, more experiments are required to evaluate the robustness and capabilities of the detector in a wider variety of situations.
|
||||||
|
The goal for this paper is the submission to a major conference in the next term.
|
||||||
|
|
||||||
|
\section{Spring 2023}
|
||||||
|
This term will have a dual goal.
|
||||||
|
On one hand, finishing the \gls{dsd} paper and submitting it to a conference.
|
||||||
|
On the other, start working on the single-source multi-measure capture system.
|
||||||
|
The current capture system is composed of a PoE box that is placed in series with the machine to monitor.
|
||||||
|
This system is reliable and serves its purpose perfectly, but it is not possible to fit in a more constrained space or able to measure multiple consumptions at once.
|
||||||
|
The single-source multi-measure system aims for integration in the machine with minimal modification to allow for easy \textit{drop-in} installation.
|
||||||
|
The goal could be a computer's \gls{psu} or an external box with multiple measurement systems.
|
||||||
|
In any case, the design and prototyping of this new measurement system is an important part of the single-source multi-measure system.
|
||||||
|
|
||||||
|
\section{Fall 2023}
|
||||||
|
Fall 2023 will be dedicated to designing and evaluating the single-source multi-measure system.
|
||||||
|
The challenge of this work is to enable the processing of multi-variate time series to yield better results.
|
||||||
|
The system's performances will be put in perspective with the capabilities of the DSD (single-source single-measure).
|
||||||
|
A series of experiments will also provide a complementary evaluation of the performances of these new techniques.
|
||||||
|
The experiments will be collected in a paper with a publication aimed at the next term.
|
||||||
|
|
||||||
|
\section{Winter 2024}
|
||||||
|
After evaluating the single-source multi-measure system, a paper will summarize the findings and present the solution.
|
||||||
|
This term will also be dedicated to beginning the design of the multi-source single-measure system.
|
||||||
|
For this third system, the capture system is already available.
|
||||||
|
The workload is mainly centred on the design of the processing algorithm.
|
||||||
Loading…
Add table
Add a link
Reference in a new issue