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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Detection Method of Hardware Trojan Based on Wavelet Noise Reduction and Neural Network
verfasst von : Xiaopeng Li, Fei Xiao, Ling Li, Jiangjiang Shen, Fengchen Qian
Erschienen in: Cloud Computing and Security
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Abstract
As there are multiple noise exist in data acquisition of chip power consumption, in order to ensure the reliability of the data, a circuit with Trojan logic is written in FPGA and the power consumption data is extracted based on AES circuit. Aiming at the influence of noise on hardware Trojan detection, a power reduction algorithm based on wavelet transform is proposed, and the optimal parameters are chosen to reduce the noise effects. To solve the problem that the feature recognition model has a great influence on the accuracy of the detection in the process of chip normal detection and hardware Trojan recognition, a hardware Trojan recognition algorithm based on neural network is proposed, which can distinguish the data from each other and detect the Trojan after data de-noising. According to the experiment, it shows that the identification rate of hardware Trojan in chip is larger than 90%, and the consumption data which size greater than 0.05% can be identified.