Jean-Luc Gaudiot, a professor of electrical engineering and computer science at the University of California, Irvine, has served the Computer Society for many years in a variety of offices, which has given him experience in many activities of the Society. He currently serves as a Vice President for Publications and is on the Board of Governors. He was 2013 Vice President for Educational Activities. He was a founder and the first editor in chief of IEEE Computer Architecture Letters and IEEE Transactions on Computers, chair of the IEEE Computer Society Technical Committee on Computer Architecture for two terms, and program committee and general chair of many major conferences. Gaudiot is a Computer Society Golden Core member and a Fellow of the IEEE and the AAAS.

Before joining UCI in 2002, where he was department chair for six years, Gaudiot was a professor of electrical engineering at the University of Southern California. His industrial experience includes software engineering at Teledyne Controls and design of innovative processor architectures at TRW. Gaudiot has more than 250 refereed publications in the field of computer architecture. He has a PhD in computer science from the University of California, Los Angeles.


Jean-Luc Gaudiot
UCI Distinguished Professor
University of California, Irvine
IEEE Computer Society President Emeritus

Enhancing Computer Security with Hardware-level Malware Detection

In the past decades, computer design has prioritized performance, cost reduction, and energy efficiency over security. Meanwhile, malicious attacks have surged with the ever-increasing number of Internet-connected devices. Traditional antivirus software struggles to combat these attacks, particularly those exploiting hardware vulnerabilities. We introduce an additional layer of malware detection at the hardware level, monitoring semantic and sub-semantic behaviors to enhance system security. We present a real-time malware detection system monitoring microarchitectural features to detect anomalies indicative of attacks like Rowhammer and Spectre. Our experiments demonstrate scalability and promising detection accuracy. Future research aims to extend detection to GPU and other hardware vulnerabilities, emphasizing proactive, multi-layered defense mechanisms to counter evolving malware threats.