Department
Computer Science and Cybersecurity
Document Type
Poster
Abstract
Hardware Trojans (HTs) are malicious modifications inserted into integrated circuits that can compromise system functionality, leak sensitive information, or cause system failures. Detecting these threats has become increasingly important as FPGA-based systems are widely used in critical applications. Several research efforts have explored machine learning and anomaly detection techniques to identify malicious hardware modifications. This poster reviews multiple detection approaches proposed in recent studies and examines how different techniques detect Trojan activity using behavioral monitoring, structural analysis, and machine learning classification. By comparing these methods, the poster highlights the strengths and limitations of current FPGA hardware Trojan detection strategies.
Publication Date
Spring 4-9-2026
Recommended Citation
Said, Y. & Ahmed, S. (2026, April 9). Self-defending FPGA Systems: Real-time hardware Trojan detection and autonomous recovery using embedded machine learning variability [Poster presentation]. Student Research Conference Spring 2026, Saint Paul, MN, United States. https://metroworks.metrostate.edu/student-scholarship/38
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
Spring 2026: Student Research Conference