The current state-of-the-art ASIC industry heavily relies on the globalized fabrication processes and hardware supply-chain model. While this benefits both participants and their global economy, the security of the underlying hardware is compromised due to various emerging hardware security threats such as overproduction, hardware trojan insertion, reverse engineering, IP theft, firmware modifications, and counterfeiting. These security threats have become a significant concern due to the rapid increase of IoT devices used in applications such as human health, public transportation, autonomous vehicles, and environmental monitoring. Hardware at the heart of the critical infrastructure consists of various hardware IPs and assets, which their continuous reliable function is deemed necessary to ensure the security of a given nation, its economy, and the public’s health and safety. There are no off-the-shelf software or scripts available for deploying the hardware security primitives. Dr. Salehi's talk is focused on novel and effective solutions to address some of these challenges. He will describe his recent efforts in developing novel approaches for securing IoT hardware and firmware supply chain. First, Dr. Salehi will present an effective approach to resist various state-of-the-art hardware reverse engineering attacks. Additionally, he will introduce a novel defense-in-depth approach using emerging beyond-CMOS devices to add an extra layer of protection by resisting powerful AI-assisted power side-channel attacks. Furthermore, Dr. Salehi will highlight the importance of firmware security by proposing an indirect and stealthy attack via firmware reverse engineering. He will demonstrate the potential impacts of the proposed attack and discuss a few practical suggestions for the detection and mitigation of firmware attacks. Finally, he will discuss the future direction of his research, which aims to bridge the ongoing research in deep learning and innovative hardware design to increase the security coverage of IoT hardware. Exciting educational opportunities will be enabled by the development of novel hardware security measures and research of machine learning in the context of security. The broader economic and societal impacts include the feasibility of improved security and energy efficiency towards privacy-preserving IoT devices.
Soheil Salehi is a tenure-track assistant professor in the Electrical and Computer Engineering (ECE) Department at the University of Arizona. Before joining the University of Arizona, Soheil was an NSF-Sponsored Computing Innovation Fellow (CIFellow) and Postdoctoral Research Fellow in the Accelerated, Secure, and Energy-Efficient Computing (ASEEC) Laboratory and the Center for Hardware and Embedded Systems Security and Trust (CHEST) at the University of California, Davis. Salehi received his Ph.D. and M.S. degrees in ECE from the University of Central Florida in 2016 and 2020, respectively. He also received a B.S. degree from the Isfahan University of Technology, Isfahan, Iran, in 2014. Salehi has published his research in prestigious journals, such as IEEE Transactions on Emerging Topics in Computing (TETC) and IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), as well as top-tier conferences in the fields of computing and security including Network and Distributed Systems Security (NDSS) Symposium and Design Automation Conference (DAC). He was the sole recipient of the UCF Excellence by a Graduate Teaching Assistant at the university level in 2016. Salehi was nominated for the 30-under-30 Award at UCF in 2020 and the Postdoctoral Research Excellence Award at UCD in 2022.