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Data-Driven Modeling and Analysis for Trustworthy Cyber-Physical Systems

Creative Commons 'BY' version 4.0 license

In the age of digitization, a layer of cyber software sits on a hardware circuit and controls the physical systems around us. The tight integration of cyber and physical components is referred to as Cyber-Physical Systems (CPS). The interactions between cyber and physical components brings unique challenges which traditional modeling tools struggle to resolve. Remarkably, they often fail to model the unintentional physical manifestation of cyber-domain information flows (side-channel signals), resulting in trust issues in the system.

This thesis takes a data-driven approach to model CPS behavior when exposed to various information flows. First, we demonstrate how to extract valuable cyber-domain information by recording the acoustic noise generated by a DNA synthesizer. Then, we consider an integrated circuit as a CPS by itself and monitor the chip through electromagnetic and power side-channels to detect hardware Trojans (HT) in the chip.

HT is a malicious modification of the hardware implementation of a circuit design which may lead to various security issues over the life-cycle of a chip. One of the major challenges for HT detection is its reliance on a trusted reference chip (a.k.a golden chip). However, in practice, manufacturing a golden chip is costly and often considered infeasible. This thesis investigates a creative neural network design and training methodology which eliminates the need for a golden chip. Furthermore, it proposes using hierarchical temporal memory (HTM) as a data-driven approach which can be updated over the chip's life-cycle and uses that for run-time HT detection.

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