diff --git a/PhD/research_proposal/futurwork.tex b/PhD/research_proposal/futurwork.tex index 18db596..faff6b1 100644 --- a/PhD/research_proposal/futurwork.tex +++ b/PhD/research_proposal/futurwork.tex @@ -37,28 +37,40 @@ The work on \gls{dsd} is the fundation for the planned development of more speci \end{figure} \section{Single-Source, Multi-Measure} -The global power consumption of a machine does not always tell the full story about its activity. +The global power consumption of a machine does not fully describe its activity. In an embedded system, the power consumption can be attributed to different components, each with its specific activity. -For the simplest systems performing one specific task --- such as \gls{rtu} ---, the activity of each component is often correlated. -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. +For the simplest systems performing one specific task, the activity of each component is often correlate with each other. +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 Mode \textit{A} pattern. For more complex systems, different components can be in different modes to accommodate the multi-tasking nature of the global activity. -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. -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. +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. +For example, if the bootup sequence of a general-purpose computer shows a significant \gls{cpu} activity but no \gls{hdd} activity, it could indicate a failure to boot or an attacker booting the system from external storage. Access to each component's individual power consumption opens the way to a more granular understanding of the machine's activity. -However, the new nature of the captured data requires an evolution of the techniques developed before. +However, the multivariate aspect of the captured data requires an evolution of the detection techniques. \subsection{Problem Statement} Differentiating between the different components to better understand the activity of a machine is a valuable capability associated with a new problem. \begin{problem-statement}[Single-Source Multi-Measure] - 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 $t[i]$\agd{fix equation overflow} + Given a discretized, multivariate time series $ts$ and a set of $n$ components for each of $m$ patterns $P=\{\{\chi\},P_1=\{P_{1,1},\dots, P_{1,n}\},\dots,$ + $P_m=\{P_{m,1},\dots, P_{m,n}\}\}$, identify an injective mapping $m_{SSMM}:\mathbb{N}\longrightarrow P$ such that every sample $ts[i]$ 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 $t$. \end{problem-statement} -The time series $t$ is a discretized, multivariate, real-valued time series. -Each sample $t[i]$ is a vector or $n$ component representing the value of each dimension of $t$ at a point in time. -Each pattern in $P$ contain multiple pattern components and represent a global pattern across all dimension of $t$. -Thus, the number of components of each pattern must be equal to the dimensions of $t$. +The time series $ts$ is a discretized, multivariate, real-valued time series. +$ts$ is composed of $n$ dimensions with the $j^{th}$ dimension referred to as $ts_j$. +Each sample $ts[i]$ is a vector or $n$ component representing the value of each dimension of $t$ at a point in time. +The items of the set $P$ are sets of patterns $P_j$ with $j\in[1,m]$. +Each set of patterns $P_j$ is associated with one component of a global pattern. +In other words, each component $P_{j,k}$ represent a the pattern $j$ along the $k^{th}$ dimension of $ts$. +Thus, the number of components of each pattern must be equal to the dimensions of $ts$. +Figure \ref{fig:notation} illustrate the $ts$ and $P$ objects. + +\begin{figure} + \centering + \includegraphics[width=0.9\textwidth]{images/ssmm_illustration.pdf} + \caption{Notations for the multivariate time series and the patterns set.} + \label{fig:notation} +\end{figure} \subsection{Applications} The goal of the multi-measure setup is dual. diff --git a/PhD/research_proposal/glossaries.tex b/PhD/research_proposal/glossaries.tex index 1a90094..391db2f 100644 --- a/PhD/research_proposal/glossaries.tex +++ b/PhD/research_proposal/glossaries.tex @@ -31,3 +31,4 @@ \newacronym{cnn}{CNN}{Convolutional Neural Network} \newacronym{dpa}{DPA}{Differential Power Analysis} \newacronym{ics}{ICS}{Industrial Control System} +\newacronym{hdd}{HDD}{Hard Drive Disk} diff --git a/PhD/research_proposal/images/ssmm_illustration.pdf b/PhD/research_proposal/images/ssmm_illustration.pdf new file mode 100644 index 0000000..62be28f Binary files /dev/null and b/PhD/research_proposal/images/ssmm_illustration.pdf differ diff --git a/PhD/research_proposal/images/ssmm_illustration.svg b/PhD/research_proposal/images/ssmm_illustration.svg new file mode 100644 index 0000000..4dbb9f8 --- /dev/null +++ b/PhD/research_proposal/images/ssmm_illustration.svg @@ -0,0 +1,1280 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/PhD/research_proposal/proposal.tex b/PhD/research_proposal/proposal.tex index 9fee643..7a3b7fe 100644 --- a/PhD/research_proposal/proposal.tex +++ b/PhD/research_proposal/proposal.tex @@ -5,6 +5,7 @@ % 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. @@ -202,6 +203,7 @@ \input{futurwork} \input{timetable} \input{conclusion} + %---------------------------------------------------------------------- % END MATERIAL % Bibliography, Appendices, Index, etc.