\chapter{Conclusion}
The problem of leveraging power side-channel analysis to defend embedded systems presents unique capabilities.
Contrary to current common \glspl{ids} techniques, physics-based security is not built on purpose-made actionable data.
The very nature of the input information sets this technique aside.
Power consumption is closely related to instruction execution, making it a good proxy variable for machine activity.
Moreover, power is easy and cheap to measure reliably at a high sampling rate, enabling analysis of any machine consuming electricity.
Finally, a sequence of instructions is generally related to a unique power consumption pattern.
This \textit{one-to-one} relationship allows us to consider the power consumption as a signature for software of machine activity.
However, power consumption is not an actionable information.
Little can be extracted from the raw time series format about the machine's activity or integrity.
To enable further analysis, a set of algorithms is required for both runtime online analysis and offline monitoring of specific activity.

The full range of capabilities remains to be discovered.
Successful runtime monitoring enables the detection of activity policy violations, anomalous activity detection, machine failure detection or distributed attacks.
Pre-OS monitoring enables the detection of boot process violation at a level where common \glspl{hids} are not enabled yet and \glspl{nids} are blind.
Developing robust and practical time series analysis techniques for the specific application of activity recognition from machine's power consumption would enable the exploration of all these applications.
Among all the many possible directions, this proposal presents the problems of activity recognition as the main stepping stone in the development of physics-based \glspl{ids}.
