In this paper, we propose a real-time anomaly detection framework for an NoC-based many-core architecture. We assume that, processing cores and memories are safe and anomaly is included through communication medium i.e router. The paper targets three different attacks namely traffic diversion, route looping and core address spoofing attacks. The attacks are detected by using Machine Learning techniques. Comprehensive analysis on machine learning algorithms suggests that, Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) have better attack detection efficiency. It has been observed that both algorithms have accuracy in the range of 94% to 97%. Additional hardware complexity analysis advocates SVM to be implemented on hardware. To test the framework, we implement condition-based attack insertion module, attacks are performed intra and inter-cluster. The proposed real-time anomaly detection framework is fully placed and routed on Xilinx Virtex-7 FPGA. Post place and route implementation results show that SVM has 12% to 2% area overhead and 3% to 1% power overhead for the Quad-core and Sixteen-core implementation, respectively. It is also observed that it takes 25% to 18% of the total execution time to detect anomaly in transferred packet for Quad-core and Sixteen-core, respectively. The proposed framework achieves 65% reduction in area overhead and is 3 times faster compared to previous published work.
Amey Kulkarni, Youngok Pino, Matthew French, and Tinoosh Mohsenin, "Real-Time Anomaly Detection Framework for Many-Core Router through Machine Learning Techniques", Journal of Emerging Technologies in Computing Systems, November 2015 (In Press)
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