Hardware Trojan Detection Technique Based on SOM Neural Network
Ning Wen, Jian Wang, Tao Zhang · 2018
In this paper, we focus on hardware Trojans inserted into IC (Integrated Circuit) chips during IC design. We propose a hardware Trojan detection technique which is based on the chip temperature characterization by using SOM neural network. This method can achieve a high detection rate for Trojans without "gold chips". First, we use a tool called HotSpot to get the steady-state heatmap from running IC. Then, we make use of 2DPCA (Dimensional Principal Component Analysis) to extract the features of the heatmap profile and feed them into a SOM (Self Organizing Map) neural network. Finally, the SOM neural network automatically distinguishes Trojan-free chips from Trojaninfected chips. The experimental results show that our method can achieve high detection rate, with 20% chip PV (Process Variability), for all centralized Trojans which are located at different positions.