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A flexible online checking technique to enhance hardware Trojan horse detectability by reliability analysis

Publication Type : Journal Article

Thematic Areas : Medical Sciences

Publisher : IEEE Transactions on Emerging Topics in Computing

Source : IEEE Transactions on Emerging Topics in Computing, IEEE Computer Society, Volume 5, Number 2, p.260-270 (2017)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026626321&doi=10.1109%2fTETC.2017.2654268&partnerID=40&md5=6976312a484dd54a335209ade2ba0ed6

Keywords : Benchmark circuit, Computer circuits, Detection coverage, Electronic design, Fault propagation, Hardware, hardware security, malware, Manufacturing practices, Online checking, Online monitoring, reliability analysis, Systems engineering, Test vector generations

Campus : Kochi

School : School of Medicine

Department : Plastic & Reconstructive Surgery

Year : 2017

Abstract : Hardware Trojan Horses have emerged as great threats to modern electronic design and manufacturing practices. Because of their inherent surreptitious nature, test vector generation to detect hardware Trojan horses is a difficult problem. Efficient online detection techniques can be more effective in detection of hardware Trojan horses. In this paper, we propose a low-overhead detection technique which inserts malicious logic detection circuitry at netlist sites chosen by an algorithm that employs an intelligent and accurate analysis of fault propagation through logic gates. Proactive system-level countermeasures can be activated on detection of malicious logic, thereby avoiding disastrous system failure. Experimental results on benchmark circuits show close to 100 percent HTH detection coverage when our proposed technique is employed, as well as acceptable overheads. © 2016 IEEE.

Cite this Research Publication : R. S. Chakraborty, Pagliarini, S., Mathew, J., Rajendran, S. R., and Devi, M. N., “A flexible online checking technique to enhance hardware Trojan horse detectability by reliability analysis”, IEEE Transactions on Emerging Topics in Computing, vol. 5, pp. 260-270, 2017.

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