A method to detect hardware Trojan in gate-level netlist is proposed using deep learning technique. The paper shows that it is easy to identify genuine nodes and Trojan-infected nodes based on controllability and transition probability values of a given Trojan-infected circuit. The controllability and transition probability characteristics of Trojan-infected nodes show large inter-cluster distance from the genuine nodes so that it is easy to cluster the nodes as Trojan-infected nodes and genuine nodes. From a given circuit, controllability and transition probability values are extracted as Trojan features using deep learning algorithm and clustering the data using k-means clustering. The technique is validated on ISCAS’85 benchmark circuits, and it does not require any golden model as reference. The proposed method can detect all Trojan-infected nodes in less than 6 s with zero false positive and zero false negative detection accuracy.
CITATION STYLE
Reshma, K., Priyatharishini, M., & Nirmala Devi, M. (2019). Hardware trojan detection using deep learning technique. In Advances in Intelligent Systems and Computing (Vol. 898, pp. 671–680). Springer Verlag. https://doi.org/10.1007/978-981-13-3393-4_68
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