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update proposal

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Arthur Grisel-Davy 2023-06-13 21:50:12 -04:00
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PhD/research_proposal/futurwork.tex
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\chapter{Planned Work}\label{chap:futurwork}
All the work achieved in the preliminary work serves as the foundation for the planned work.
The thesis will focus on the state detection problem.
Detecting the state of a system is a stepping stone in the construction of specialized tools.
The thesis will focus on the state detection problem under various input data and detection requirements.
Detecting the state of a system constitute a stepping stone in the construction of specialized tools for physics-based security.
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.
In this sense, solving the state detection problem enables a deeper investigation of power consumption by making the data actionable.
The different machines and data measurement design lead to different problems to solve and different detection capabilities.
This chapter described the planned research topics with their problem statement and a description of the motivation and expected results.
The different machines and data measurement designs lead to different problems to solve and different detection capabilities.
This chapter described the problems to study with their problem statement as well as the motivations and expected results.
The problems are discretized based on the input data and measured machines that constitute the power trace.
A single sensor only measure the power flowing through one cable.
It is possible to combine sensores to measure multiple related consumptions --- for example, the consumptions of different components in the same machine.
In this case, the problem is called \textit{multi-measure} and the resulting input data is multivariate trace.
It is also possible to place the sensor on a power cable that provide power to multiple machines.
In this case, the problem is called \textit{multi-sources} and the resulting input data is an aggregate of multiple traces.
The difference between machines and components is a fine and blury line as the description of a machine often fits individual components.
In this thesis, a component is a system that expects instructions from a central unit while a machine run its own software.
For example, at a macroscopic scale, a graphics card does not take the initiative on its own to run any software and expect instructions from the rest of the \gls{pc}.
\section{Single-Source, Single-Measure}
The \gls{dsd} shows promising results in an experimental setup.
The \gls{dsd} --- example of a Single-Source Single-Measure problem --- shows promising results in an experimental setup.
To this date, the experiments have focused on the detection of simple global states.
The global state are usualy \textit{OFF}, \textit{ON}, \textit{BOOT}, \textit{HIGH LOAD}.
Depending on the machine, other states like \textit{FIRMWARE FLASH}, \textit{SLEEP} or a specific activity mode can also be detected.
The experiments focus on the deployment of general-purpose computers, network switches, and \gls{wap}/routers.
In the next months, the goal for the \gls{dsd} is to evaluate the performances of the runtime state detection.
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).
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.
These results will be compiled for the publication of an article.
The work on \gls{dsd} is the base for the planned development of more specific applications of the same principle of physics-based monitoring.
The experiments focus on the deployment to general-purpose computers, network switches, and \gls{wap}/routers.
In the next months, the goal for the \gls{dsd} is to evaluate the performances of the runtime state detection in broaders and more exhaustives conexts.
The current accuracy and edit distance performances (see Figure \ref{fig:dsd_acc}) illustrate the capabilities of the \gls{dsd} for the detection of well defined states --- i.e. states associated with a striking variation of average power consumption.
However, in order to provide a useful and reliable runtime labeling of the a machine's activity, the \gls{dsd} must achieve similar results with a more diverse selection of states.
The work on \gls{dsd} is the fundation for the planned development of more specific applications of the same principle of physics-based monitoring.
\begin{figure}
\centering
\includegraphics[width=\textwidth]{images/dsd_acc}
\caption{Current results of the DSD algorithm on several datasets..}
\caption{Current results of the DSD algorithm on several datasets.}
\label{fig:dsd_acc}
\end{figure}
\section{Single-Source, Multi-Measure}
The global power consumption of a machine does not necessarily tell the full story about its activity.
In an embedded system, the power consumption can be assigned to different components, each with its specific activity.
For the simplest systems performing one specific task \agd{give examples}, the activity of each component is often consistently correlated/agd{weird sentence}.
The global power consumption of a machine does not always tell the full story about 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 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.
@ -43,7 +51,7 @@ However, the new nature of the captured data requires an evolution of the techni
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 $t[i]$
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}
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}
@ -99,3 +107,6 @@ For example, an assembly line can leverage hundreds of conveyor belt drivers, ro
Each type of system is simple in its design and task.
However, adding a designated power monitoring measurement device to each individual system is costly, maintenance-heavy, and it multiplies the potential points of failure.
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.
\section{Conclusion}
\agd{to be filled}