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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Side-Channel Signal Analysis for Securing Embedded and Cyber-Physical Systems
Committee:
Dr. Alenka Zajic, ECE, Chair , Advisor
Dr. Milos Prvulovic, CS, Co-Advisor
Dr. Morris Cohen, ECE
Dr. David Anderson, ECE
Dr. Azadeh Ansari, ECE
Dr. Alessandro Orso, CS
Abstract: Side-channels cause unintentional information leakage as a side-effect of hardware activity due to legitimate program execution. While attackers have traditionally exploited side-channel analysis for extracting sensitive information from target systems, recent research has utilized side-channels for non-adversarial monitoring of program execution. This approach can be especially useful for securing resource-constrained security-critical embedded systems. This thesis develops methods that leverage electromagnetic (EM) side-channel signals for non-adversarial and non-intrusive monitoring of embedded and cyber-physical systems. Our research provides techniques for identifying anomalous/malicious program behavior by detecting deviations in EM emanations and presents a framework for end-to-end basic-block program execution tracking by monitoring the device's EM side-channel signal. In this thesis, we 1) designed an intrusion detection system that learns a dictionary of reference EM signatures and exploits the dictionary for identifying anomalous/malicious program behavior, 2) designed neural network to model the monitored device's EM side-channel signal and detect stealthy malware activities through deviations in EM emanations, 3) designed a novel framework that performs basic-block program execution tracing by monitoring device's EM side-channel signal, and 4) demonstrated that even a single instruction deviation in program execution can be detected with high accuracy via EM side-channel signals captured by a readily available measurement device.
