Publication Type:

Journal Article

Source:

Communications in Computer and Information Science, Springer Verlag, Volume 746, p.379-386 (2017)

ISBN:

9789811068973

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034590931&doi=10.1007%2f978-981-10-6898-0_31&partnerID=40&md5=38e4670356a0d90375278596091222fc

Keywords:

Consistency analysis, Detection and diagnosis, Electric network analysis, Gate levels, Hardware, hardware security, Hardware Trojan detection, Integrated circuits, Intrinsic process, malware, Semiconductor device manufacture, Semiconductor device testing, Semiconductor industry, Test Pattern, Transition probabilities

Abstract:

Hardware Trojans (HTs) have become a major threat to the modern fabless semiconductor industry. This has raised serious concerns over integrated circuits (IC) outsourcing. HT detection and diagnosis is challenging due to the diversity of HTs, large number of gates in modern ICs, intrinsic process variation (PV) in IC design and the high cost of testing. An efficient HT detection and diagnosis scheme based on selective segmentation is proposed in this work. It divides the large circuit into small sub-circuits and applies consistency analysis of gate-level properties. In addition, Transition probability (TP) estimation for each node is employed and performed segmentation on the least probable transition nodes. In order to further enhance the detection, optimized test vectors are chosen during the procedure. Based on the selected segments, HTs are detected correctly by tracing gate level properties. © 2017, Springer Nature Singapore Pte Ltd.

Notes:

cited By 0; Conference of 5th International Symposium on Security in Computing and Communications, SSCC 2017 ; Conference Date: 13 September 2017 Through 16 September 2017; Conference Code:204689

Cite this Research Publication

A. K. Sashank, Reddy, H. S., Pavithran, P., Akash, M. S., and M. Devi, N., “Hardware Trojan Detection Using Effective Test Patterns and Selective Segmentation”, Communications in Computer and Information Science, vol. 746, pp. 379-386, 2017.

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