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A novel hardware Trojan detection based on BP neural network

With the development of integrated circuits, chip security has become an important part of IC field. Hardware Trojan, as a major threat to the security of chips have been widely concerned. At present, there have several hardware Trojan detection method: reverse anatomy, function test, bypass signal analysis, etc. The bypass signal analysis based on power consumption is the most widely used method, but the problem is that the ability of feature extraction is not satisfied. However, the BP neural network has strong ability of nonlinear mapping and adaptive learning, which can better retain and extract features in power consumption analysis. This paper uses the BP neural network to establish mathematical model of feature extraction, and extract nonlinear feature from power consumption information. The power acquisition and feature extraction experiment platform is based on FPGA, The experimental results show that the hardware Trojan detection method based on BP neural network is effective.