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Golden-Chip Free Side Channel Delay Analysis Test for Hardware Trojan and Recycled IC Detection

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dc.contributor.advisor Sasan, Avesta
dc.contributor.author Vakil, Ashkan
dc.creator Vakil, Ashkan
dc.date 2021-04-29
dc.date.accessioned 2021-09-28T11:49:43Z
dc.date.available 2021-09-28T11:49:43Z
dc.identifier.uri http://hdl.handle.net/1920/12089
dc.description.abstract The distributed manufacturing supply chain of Integrated Circuits (IC) introduces many vulnerabilities during IC's life cycle. An adversary in an untrusted foundry can exploit these weaknesses to design malicious hardware attacks that target the integrity, reliability, and trustworthiness of fabricated ICs. This work introduces a set of physical-aware and learning-assisted modeling techniques, followed by test methodologies, for Hardware security in the post-fabrication stage. The proposed detection approach targets to identify Hardware-Trojan infected chips and recycled- ICs. Unlike the prior art, this ow does not require a Golden fabricated chip as a fingerprint to compare the side-channel signals. Instead, by modeling the voltage drop and voltage noise pre-fabrication and training a Neural Network post-fabrication, our proposed technique can improve the timing model collected during timing closure and produces a Neural Assisted Golden Timing Model (NGTM) for side-channel delay-signal analysis. The Neural Network acts as a process tracking watchdog for correlating the static timing data (produced at design time) to the delay information obtained from a clock frequency sweeping test. Proposed detection ow enables Hardware Trojan detection close to 90%, and 100% recycled-IC detection in the simulated scenarios. en_US
dc.language.iso en en_US
dc.subject hardware Trojan en_US
dc.subject neural network en_US
dc.subject process variation en_US
dc.subject clock frequency sweeping test en_US
dc.subject side channel analysis en_US
dc.subject process drift en_US
dc.title Golden-Chip Free Side Channel Delay Analysis Test for Hardware Trojan and Recycled IC Detection en_US
dc.type Dissertation en_US
thesis.degree.name Doctor of Philosophy in Electrical and Computer Engineering en_US
thesis.degree.level Doctoral en_US
thesis.degree.discipline Electrical and Computer Engineering en_US
thesis.degree.grantor George Mason University en_US


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