The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: ACM Student Research Competition, Undergraduate
Author: Rye Stahle-Smith (University of South Carolina), Rasha Karakchi (University of South Carolina)
Supervisor: Rasha Karakchi (University of South Carolina)
Abstract: Field-programmable gate arrays (FPGAs) in reconfigurable systems face escalating security threats from malicious bitstreams capable of causing denial-of-service, data leakage, or covert operations. Traditional detection methods often require source code or netlists, limiting their applicability for real-time protection.
We present a supervised machine learning approach that directly analyzes FPGA bitstreams at the binary level, enabling rapid detection without design-level access. Using byte frequency analysis, truncated singular value decomposition (TSVD), and SMOTE balancing, we developed and evaluated multiple classifiers on a dataset of 122 benign and malicious configurations for the Xilinx PYNQ-Z1 board. Random Forest achieved a macro F1-score of 0.97, validating the method’s effectiveness for resource-constrained devices.
The final model was deployed on PYNQ for integrated, on-device analysis. During the poster session, we will outline our detection pipeline, dataset preparation process, and performance results, emphasizing the novelty of binary-level analysis and its implications for real-time Trojan detection in embedded systems.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF