Publication Type : Journal Article
Publisher : Advances in Intelligent Systems and Computing
Source : Advances in Intelligent Systems and Computing, Springer Verlag, Volume 898, p.671-680 (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062283011&doi=10.1007%2f978-981-13-3393-4_68&partnerID=40&md5=44b52990dd1de4732d42d04d190bbb37
ISBN : 9789811333927
Keywords : Benchmark circuit, Controllability, Deep learning, Detection accuracy, False negatives, False positive, hardware security, Hardware Trojan detection, Inter clusters, K-means clustering, Learning algorithms, Learning techniques, probability, Signal processing, Soft computing, Transition probabilities
Campus : Coimbatore
School : Department of Electronics and Communication Engineering, School of Engineering
Department : Electronics and Communication
Year : 2019
Abstract : 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. © Springer Nature Singapore Pte Ltd. 2019.
Cite this Research Publication : K. Reshma, Priyatharishini, M., and M. Devi, N., “Hardware trojan detection using deep learning technique”, Advances in Intelligent Systems and Computing, vol. 898, pp. 671-680, 2019.