Abstract
As the internationalization of Integrated Circuit (IC) production increased, the inclusion of deliberately stealthy modification called hardware Trojans has also escalated. A hardware Trojan detection method that works at the gate-level using the netlist of the circuit under test is presented in this paper. The unsupervised machine learning algorithm, K-Means classification is used for categorization. Every net of the circuit is analyzed to determine if the net is genuine or is Trojan infected by the extraction of seven relevant features from every net. The technique has been validated on ISCAS’85 benchmark circuits and parameters like true positive (TP), false negative (FN) and recall (TPR) have been illustrated.
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Chockaiah, N.S., Kayal, S.K.S., Malar, J.K., Kirithika, P., Devi, M.N. (2021). Hardware Trojan Detection Using Machine Learning Technique. In: Gunjan, V.K., Zurada, J.M. (eds) Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Advances in Intelligent Systems and Computing, vol 1245. Springer, Singapore. https://doi.org/10.1007/978-981-15-7234-0_37
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DOI: https://doi.org/10.1007/978-981-15-7234-0_37
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