Non-Intrusive system behavior tracing using power consumption
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This thesis presents a novel non-intrusive method of creating a map of black-box systems’ behavior to commands by observing the power consumption of the system. We expand our method in the form of a flexible framework which supports communication abstraction layer to send commands on different channels. The framework also integrates data processing and the opportunity of adding a validation module that framework users can utilize to detect anomalies in the system. The framework furthermore builds on the idea of anomaly detection by providing two operating modes: one for generating training data for machine learning models, and one for run-time validation of the system’s behavior. From the experiments, we confirm the effectiveness of the framework for creating a map of the system’s power consumption against a series of queries. Our experiments also show some of the challenges presented with this method when different commands induce similar behaviour in the system.
Cite this version of the work
Adan Flores (2020). Non-Intrusive system behavior tracing using power consumption. UWSpace. http://hdl.handle.net/10012/15841