  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.
