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.
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References
Jacob, N., Merli, D.: Hardware Trojan: current challenges and approaches. IET Comput. Digit. Tech. 8(6), 264–273 (2014)
Ni, L., Li, S., Ma, R., Wei, P.: Hardware Trojans detection and protection. Digit. Commun. 41(1), 59–63 (2014)
Xu, Q., Jiang, X., Yao, L., Zhang, Z., Zhang, C.: Overview of the detection and prevention study of hardware Trojans. Chin. J. Netw. Inf. Secur. 3(4), 1–13 (2017)
Wang, L., Luo, H., Yao, R.: Side-channel analysis-based detection approach of hardware Trojans. J. South China Univ. Technol. (Nat. Sci. Ed.) 40(6), 6–10 (2012)
Zhao, Z., Ni, L., Li, S.: Detection method of hardware Trojans power consumption. J. Beijing Univ. Posts Telecommun. 38(4), 128–132 (2015)
Li, H., Zhao, Y., Yang, R., et al.: Hardware Trojan detection optimization based on wavelet de-noising data preprocessing. Comput. Eng. Appl. 53(1), 49–53 (2017)
Wang, X., Salmani, H., Tehranipoor, M., et al.: Hardware Trojan detection and isolation using current integration and localized current analysis. In: Proceedings of IEEE International Symposium on Defect and Fault Tolerance of VLSI Systems, pp. 87–95. IEEE Computer Society, Washington, DC (2008)
Chakraborty, R.S., Wolff, F., Paul, S., Papachristou, C., Bhunia, S.: MERO: a statistical approach for hardware trojan detection. In: Clavier, C., Gaj, K. (eds.) CHES 2009. LNCS, vol. 5747, pp. 396–410. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04138-9_28
Wang, J., Wang, B., Qu, M., Zhang, L.: Hardware Trojan detection based on naive Bayesian classifier. Appl. Res. Comput. 34(10), 3073–3076 (2017)
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Li, X., Xiao, F., Li, L., Shen, J., Qian, F. (2018). Detection Method of Hardware Trojan Based on Wavelet Noise Reduction and Neural Network. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11067. Springer, Cham. https://doi.org/10.1007/978-3-030-00018-9_23
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DOI: https://doi.org/10.1007/978-3-030-00018-9_23
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