Supervised Machine Learning Techniques for Trojan Detection with Ring Oscillator Network
This work addresses the need for accurate Trojan detection in semiconductor supply chains, but it is incremental as it applies existing supervised learning techniques to a known bottleneck of process variation and noise.
The paper tackles the problem of detecting hardware Trojans in integrated circuits using ring oscillator networks, comparing four supervised machine learning algorithms to improve accuracy and reduce false positives. The results show a nearly 40% reduction in false positive rate compared to existing methods while maintaining over 90% binary classification accuracy.
With the globalization of the semiconductor manufacturing process, electronic devices are powerless against malicious modification of hardware in the supply chain. The ever-increasing threat of hardware Trojan attacks against integrated circuits has spurred a need for accurate and efficient detection methods. Ring oscillator network (RON) is used to detect the Trojan by capturing the difference in power consumption; the power consumption of a Trojan-free circuit is different from the Trojan-inserted circuit. However, the process variation and measurement noise are the major obstacles to detect hardware Trojan with high accuracy. In this paper, we quantitatively compare four supervised machine learning algorithms and classifier optimization strategies for maximizing accuracy and minimizing the false positive rate (FPR). These supervised learning techniques show an improved false positive rate compared to principal component analysis (PCA) and convex hull classification by nearly 40% while maintaining > 90\% binary classification accuracy.