*********************************
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
*********************************
Title: Efficient Learning for Hardware Security Validation using Electromagnetic Side Channels
Committee
Dr. Alenka Zajić, School of Electrical and Computer Engineering, Georgia Tech (Advisor)
Dr. Matthieu Bloch, School of Electrical and Computer Engineering, Georgia Tech (Co-Advisor)
Dr. David Anderson, School of Electrical and Computer Engineering, Georgia Tech
Dr. Mark Davenport, School of Electrical and Computer Engineering, Georgia Tech
Dr. Milos Prvulovic, School of Computer Science, Georgia Tech
Dr. Mikko Lipasti, Department of Electrical and Computer Engineering, University of Wisconsin-Madison
Abstract: The authenticity of integrated circuits is of increasing concern as more steps in the device manufacturing supply chain are outsourced, especially considering current global semiconductor shortages. Common methods for integrated circuit validation rely on either destructive techniques with high resolution imaging of the circuit interconnects or functional testing of a variety of test inputs with automated test equipment. These methods are time-consuming or even intractable to detect counterfeit components or stealthy modifications of underlying circuitry. The objective of the proposed research is to combine the non-destructive monitoring advantages of standard and backscattering electromagnetic side channels with modern machine learning techniques to efficiently validate the authenticity of individual integrated circuits installed on a motherboard.
First, we apply deep learning methods to classify and detect counterfeits of major ICs on a variety of motherboards. Second, we leverage hyperspectral scanning with the backscattered EM side-channel and design a novel active learning method to detect dormant hardware trojans several times smaller than previously possible. Finally, we develop a compressed sensing approach to heavily reduce sampling for hardware trojan detection while defining a hyperspectral characterization of expected and anomalous circuits.
Reply
Reply all
Forward
