gramarly conclusion
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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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The problem of leveraging power side-channel analysis to defend embedded systems presents unique capabilities.
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Contrary to 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 technique 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 measure 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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This \textit{one-to-one} relationship allows us to consider the power consumption as a signature for software of machine activity.
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However, power consumption is not an actionable information.
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Little can be extracted from the raw time series format about the machine's activity or integrity.
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To enable further analysis, a set of algorithms is required for both runtime online analysis and offline monitoring of specific activity.
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The full range of capabilities remains to be discovered.
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Successful runtime monitoring enables the detection of activity policy violations, anomalous activity detection, machine failure detection or distributed attacks.
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On the other hand, pre-OS monitoring enables 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 applications of this technology, with many more to be discovered.
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This proposal presents some problems to study that enable the development of physics-based security.
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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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