Benjamin Tan, Ph.D., BE(Hons)
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Ph.D. Computer Systems Engineering, University of Auckland,
Dr. Benjamin Tan (he/him/his) is an Assistant Professor in the Department of Electrical and Software Engineering, University of Calgary. His research work at present focuses on improving the security of computer systems at the hardware level and understanding the implications of emerging machine learning techniques on the IC supply chain and life cycle. Prior to joining the University of Calgary, Dr. Tan was a Research Assistant Professor at New York University with the Center for Cybersecurity.
His recent research efforts include projects in collaboration with Intel, and his work has been funded by the National Science Foundation. He earned his Ph.D. at the University of Auckland, New Zealand. At the University of Auckland, he worked as a Professional Teaching Fellow and received a Student’s Choice Top Teacher Award (Top 15 in the Faculty). He has served as a coordinator and adviser for competitions at CSAW (the most comprehensive student-run cybersecurity event in the world). He is a member of IEEE and ACM.
Areas of Research
The general trend for computing systems these days is for increased integration: add more cores and more software/firmware into a system-on-chip (SoC)!
While the SoC approach provides new ways for achieving application-specific requirements through customization, the use of 3rd party IPs and increasing overall complexity can lead to potential security threats. In this line of work, I am broadly interested in coming up with new design flows and architectures that improve security. Naturally, nothing is free -- so working out how to specify security objectives and achieve them while also satisfying other requirements is the name of the game.
Hardware lies at the foundation of all computing systems -- processors, accelerators, memories -- securing hardware from attackers is paramount.
There are several problems in hardware security, including detecting hardware Trojans, Intellectual Property (IP) protection (e.g., reverse engineering), and side-channel attacks. How will the increasing capabilities of AI/ML affect hardware security? Increasing predictive capability can help with challenges like Trojan detection or malware classification. However, there is an opportunity for AI/ML to devise new strategies for attack and defense. In this line of work, I'm interested in seeing how we can formulate hardware security problems so that AI agents can start exploring the design space. This could extend to areas of research, such as logic locking, in which I have taken a recent interest.
|Course number||Course title||Semester|
|ENCM 515||Digital Signal Processors||Winter 2022|
|ENCM 511||Embedded System Interfacing||Fall 2022|
|ENCM 515||Digital Signal Processors||Winter 2023|
- Tan, B; Elnaggar, R; Fung, J; Karri, R; Chakrabarty, K. Toward Hardware-Based IP Vulnerability Detection and Post-Deployment Patching in Systems-on-Chip. (2020)
- Chowdhury, A; Tan, B; Garg, S; Karri, R. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. (2021)
- Bhandari, J; Khader Thalakkattu Moosa, A; Tan, B; Pilato, C; Gore, G; Tang, X; Temple, S; Gaillardon, P; Karri, R. IEEE/ACM International Conference On Computer Aided Design. (2021)
In the News
- GitHub's Copilot may steer you into dangerous waters about 40% of the time – study. The Register. (2021)
- GitHub's AI programming assistant can introduce security flaws. NewScientist. (2021)