Abstract
Hardware Trojans (HTs) are malicious manipulations of the standard functionality of an integrated circuit (IC). Sophisticated defense against HT attacks has become the utmost current research endeavor. In particular, the HTs whose operations depend on the rare activation condition are the most critical ones. Among other techniques, logic test by rare net excitation is advocated as one of the viable detection methods due to no extra hardware requirement. However, logic test faces a tremendous challenge of the overhead of testing configuration. This work presents a methodology based on the primary input’s impact over rare nets using transition probability to select the useful test vectors. To generate a test vector, each input’s toggle probability is calculated, which drastically minimizes the search space. The capability of rare-signal generation selects the final list of test vectors. Simulations performed in the presence of different HT triggers on different benchmark circuits, like ISCAS ’85, ISCAS ’89, and ITC ’99, show that the proposed methodology is capable of producing test vectors with significantly improved rare net coverage. Furthermore, compared to an existing technique, the proposed methodology produces average higher rare switching (around 72%) inside a netlist.
- [1] . 2017. A flexible online checking technique to enhance hardware trojan horse detectability by reliability analysis. IEEE Transactions on Emerging Topics in Computing 5, 2 (
April 2017), 260–270. Google ScholarCross Ref
- [2] . 2009. MERO: A statistical approach for hardware trojan detection. In Cryptographic Hardware and Embedded Systems—CHES 2009, and (Eds.). Springer, Berlin, 396–410.Google Scholar
- [3] . 2018. Hardware trojan detection in third-party digital intellectual property cores by multilevel feature analysis. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 37, 7 (
July 2018), 1370–1383. Google ScholarCross Ref
- [4] . 2019. Hardware trojan detection by stimulating transitions in rare nets. In 2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID’19). 537–538.Google Scholar
Cross Ref
- [5] . 2021. Hardware trojan horse detection through improved switching of dormant nets. ACM Journal on Emerging Technologies in Computing Systrems 17, 3 (
May 2021), Article33 , 22 pages. Google ScholarDigital Library
- [6] . 2020. An unsupervised detection approach for hardware trojans. IEEE Access 8 (2020), 158169–158183. Google Scholar
Cross Ref
- [7] . 2015. New testing procedure for finding insertion sites of stealthy Hardware Trojans. In 2015 Design, Automation Test in Europe Conference Exhibition (DATE’15). 776–781. Google Scholar
Cross Ref
- [8] . 2018. Protection against hardware trojans with logic testing: Proposed solutions and challenges ahead. IEEE Design Test 35, 2 (
April 2018), 73–90. Google ScholarCross Ref
- [9] . 2015. Introduction to hardware Trojan detection methods. In 2015 Design, Automation Test in Europe Conference Exhibition (DATE’15). 770–775. Google Scholar
Cross Ref
- [10] . 2019. Trigger identification using difference-amplified controllability and dynamic transition probability for hardware trojan detection. IEEE Transactions on Information Forensics and Security (2019). Google Scholar
Cross Ref
- [11] . 2019. Surviving information leakage hardware trojan attacks using hardware isolation. IEEE Transactions on Emerging Topics in Computing 7, 2 (2019), 253–261.Google Scholar
Cross Ref
- [12] . 2016. MERS: Statistical test generation for side-channel analysis based trojan detection. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS’16). Association for Computing Machinery, New York, NY, 130–141. Google Scholar
Digital Library
- [13] . 2018. Scalable test generation for trojan detection using side channel analysis. IEEE Transactions on Information Forensics and Security 13, 11 (
Nov. 2018), 2746–2760. Google ScholarCross Ref
- [14] . 2010. Trustworthy hardware: Identifying and classifying hardware trojans. Computer 43, 10 (
Oct. 2010), 39–46. Google ScholarDigital Library
- [15] . 2015. Hardware trojan detection using exhaustive testing of k-bit subspaces. In The 20th Asia and South Pacific Design Automation Conference. 755–760. Google Scholar
Cross Ref
- [16] . 2020. Securing cyber-physical systems from hardware trojan collusion. IEEE Transactions on Emerging Topics in Computing 8, 3 (July 2020), 655–667. Google Scholar
Cross Ref
- [17] . 2019. Efficient test generation for trojan detection using side channel analysis. In 2019 Design, Automation Test in Europe Conference Exhibition (DATE’19). 408–413. Google Scholar
Cross Ref
- [18] . 2004. Zchaff2004: An efficient sat solver. In International Conference on Theory and Applications of Satisfiability Testing. Springer, 360–375.Google Scholar
- [19] . 2020. Hardware trojan mitigation in pipelined MPSoCs. ACM Transactions on Design Automation of Electronic Systems 25, 1 (
Jan. 2020), Article6 , 27 pages. Google ScholarDigital Library
- [20] . 2013. Monte Carlo based test pattern generation for hardware trojan detection. In 2013 IEEE 11th International Conference on Dependable, Autonomic and Secure Computing. 131–136. Google Scholar
Digital Library
- [21] . 2019. A novel test vector generation method for hardware trojan detection. In 2019 32nd IEEE International System-on-Chip Conference (SOCC’19). 80–85.Google Scholar
Cross Ref
- [22] . 2018. XOR based methodology to detect hardware trojan utilizing the transition probability. In 2018 8th International Symposium on Embedded Computing and System Design (ISED’18). 215–219.Google Scholar
- [23] . 2013. Hardware trojan detection by multiple-parameter side-channel analysis. IEEE Transactions on Computers 62, 11 (
Nov. 2013), 2183–2195. Google ScholarDigital Library
- [24] . 2021. AdaTrust: Combinational hardware trojan detection through adaptive test pattern construction. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 29, 3 (2021), 544–557. Google Scholar
Cross Ref
- [25] . 2018. Hardware trojan detection using an advised genetic algorithm based logic testing. Journal of Electronic Testing 34, 4 (2018), 461–470.Google Scholar
Digital Library
- [26] . 2020. Test generation using reinforcement learning for delay-based side-channel analysis. In Proceedings of the 39th International Conference on Computer-Aided Design (ICCAD’20). Association for Computing Machinery, New York, NY, Article
109 , 7 pages. Google ScholarDigital Library
- [27] . 2004. A measure of quality for n-detection test sets. IEEE Transactions on Computers 53, 11 (2004), 1497–1503. Google Scholar
Digital Library
- [28] . 2015. Improved test pattern generation for hardware trojan detection using genetic algorithm and boolean satisfiability. In Cryptographic Hardware and Embedded Systems—CHES 2015, and (Eds.). Springer, Berlin, 577–596.Google Scholar
Digital Library
- [29] . 2013. On design vulnerability analysis and trust benchmarks development. In 2013 IEEE 31st International Conference on Computer Design (ICCD’13). 471–474.Google Scholar
Cross Ref
- [30] . 2012. A novel technique for improving hardware trojan detection and reducing trojan activation time. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 20, 1 (
Jan. 2012), 112–125. Google ScholarDigital Library
- [31] . 2020. PMTP: A MAX-SAT-based approach to detect hardware trojan using propagation of maximum transition probability. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, 1 (2020), 25–33. Google Scholar
Digital Library
- [32] . 2021. Enhancing hardware trojan detection sensitivity using partition-based shuffling scheme. IEEE Transactions on Circuits and Systems II: Express Briefs 68, 1 (2021), 266–270.Google Scholar
Cross Ref
- [33] . 2021. Hardware trojan attack in embedded memory. ACM Journal on Emerging Technologies in Computing Systems 17, 1 (
Jan. 2021), Article6 , 28 pages. Google ScholarDigital Library
- [34] . 2016. Hardware trojans: Lessons learned after one decade of research. ACM Transactions on Design Automation of Electronic Systems 22 (May 2016), 1–23. Google Scholar
Digital Library
- [35] . 2014. A low cost acceleration method for hardware trojan detection based on fan-out cone analysis. In 2014 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS’14). 1–10. Google Scholar
Digital Library
- [36] . 2016. Cost-efficient acceleration of hardware trojan detection through fan-out cone analysis and weighted random pattern technique. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35, 5 (
May 2016), 792–805. Google ScholarDigital Library
- [37] . 2018. Potential trigger detection for hardware trojans. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 37, 7 (2018), 1384–1395. Google Scholar
Cross Ref
Index Terms
Hardware Trojan Detection using Transition Probability with Minimal Test Vectors
Recommendations
Automated Test Generation for Hardware Trojan Detection using Reinforcement Learning
ASPDAC '21: Proceedings of the 26th Asia and South Pacific Design Automation ConferenceDue to globalized semiconductor supply chain, there is an increasing risk of exposing System-on-Chip (SoC) designs to malicious implants, popularly known as hardware Trojans. Unfortunately, traditional simulation-based validation using millions of test ...
TRAP-GATE: A Probabilistic Approach to Enhance Hardware Trojan Detection and its Game Theoretic Analysis
AbstractRescuing Hardware from malware attacks is a great challenge today. Moreover detecting the presence of malicious intrusion using low-cost techniques is very challenging especially when it is believed that hardware Trojans are integrated into the ...
Hardware Trojan Horse Detection through Improved Switching of Dormant Nets
Covert Hardware Trojan Horses (HTH) introduced by malicious attackers during the fabless manufacturing process of integrated circuits (IC) have the potential to cause malignant functions within the circuit. This article employs a Design-for-Security ...






Comments