Friday, April 28, 2023
3:50 – 4:50 p.m. (CST)
ETB 1020 or Zoom (see syllabus or email list for link)
CE PhD Student
Dept. of Electrical and Computer Engineering; Computer Engineering
Texas A&M University
Title: “Reinforcement Learning for Hardware Security ”
- Security threats such as hardware Trojans due to a globalized integrated circuits supply chain
- Using reinforcement learning to detect hardware Trojans efficiently and effectively
- Using reinforcement learning to evaluate hardware Trojan detection techniques accurately
Reinforcement learning (RL) has shown great promise in solving problems in novel domains, e.g., marketing, chip placement, and matrix multiplication. In this talk, I will discuss another area that has just begun to reap the powers of RL: hardware security. In particular, I will discuss two of our recent works that use RL to address the threat of hardware Trojans (HTs) in integrated circuits. HTs are malicious logic added by adversaries to harm integrated circuits. They pose a significant threat to critical infrastructures and have been the focus of much research.
In the first part of the talk, I will present a reinforcement learning (RL) agent that returns a minimal set of patterns most likely to detect HTs. Our experimental results demonstrate the efficacy and scalability of our RL agent, which significantly reduces the number of test patterns while maintaining or improving coverage compared to state-of-the-art techniques. In the second part of the talk, I will discuss how we play the role of a realistic adversary and question the efficacy of existing HT detection techniques by developing an automated, scalable, and practical attack framework. Our framework uses RL to evade eight detection techniques across two HT detection categories, demonstrating its agnostic behavior.
Using the example of HTs, our work highlights the potential of RL in solving hardware security problems. The talk will conclude with a discussion of future directions for research in this area.
Vasudev Gohil is pursuing a Ph.D. in Computer Engineering at Texas A&M University in College Station, Texas. His research interests lie at the intersection of machine learning and hardware security. He is keenly interested in examining and developing IP protection techniques and applying reinforcement learning techniques for security. Before his doctoral studies, Vasudev received a Bachelor of Technology degree in Electrical Engineering with minors in Computer Science from the Indian Institute of Technology Gandhinagar.
More on Vasudev Gohil: https://gohilvasudev.wixsite.com/website
More on CESG Seminars: HERE
Please join on Friday, 4/28/22 at 3:50 p.m. in ETB 1020 or via Zoom.
Zoom option: Links and PW in syllabus or found in email announcement.