Abstract
Machine learning techniques have the capability to explore high-dimensional feature space and find patterns that are not intuitive for analytic approaches. It is natural to apply these techniques for hardware Trojan detection to distinguish Trojan-infected designs from good designs. Almost in all aspects of hardware Trojan detection, we can tune machine learning for hardware Trojan detection. For logic testing, machine learning can help generate test vectors that are more likely to have Trojans triggered or partially activated. For side-channel analysis, machine learning can build the pattern of side-channel fingerprints of normal circuits and any outlier will be a Trojan circuit. For approaches based on structural or functional analysis, we can extract the structural or functional properties as features and train machine learning for classification. For runtime Trojan detection or monitoring, machine learning can help as long as we can extract the runtime behavior into features and train the model properly. This chapter discusses some of the successful cases of using machine learning in these aspects to inspire readers for more research in this exciting research domain.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
D. Agrawal, S. Baktir, D. Karakoyunlu, P. Rohatgi, B. Sunar, Trojan detection using IC fingerprinting, in IEEE Symposium on Security and Privacy (2007), pp. 296310
M.E. Amyeen et al., Evaluation of the quality of N-detect scan ATPG patterns on a processor, in International Test Conference (2004)
S. Bhunia et al., Hardware Trojan attacks: threat analysis and countermeasures, in IEEE Special Issue on Trustworthy Hardware (2014)
R. Chakraborty et al., MERO: a statistical approach for hardware Trojan detection, in CHES Workshop (2009)
S. Charles, Y. Lyu, P. Mishra, Real-time detection and localization of DoS attacks in NoC based SoCs, in Design Automation and Test in Europe (DATE) (2019)
J. Cruz et al., Hardware Trojan detection using ATPG and model checking, in International Conference on VLSI Design (2018)
J. Cruz, Y. Huang, P. Mishra, S. Bhunia, An automated configurable Trojan insertion framework for dynamic trust benchmarks, in Design Automation and Test in Europe (DATE) (2018)
J. Cruz, P. Mishra, S. Bhunia, The metric matters: how to measure trust, in Design Automation Conference (DAC) (2019)
F. Farahmandi, Y. Huang, P. Mishra, Trojan localization using symbolic algebra, in Asia and South Pacific Design Automation Conference (ASP-DAC) (2017), pp. 591–597
X. Guo, R.G. Dutta, P. Mishra, Y. Jin, Automatic code converter enhanced PCH framework for SoC trust verification. IEEE Trans. Very Large Scale Integr. VLSI Syst. 25(12), 3390–3400 (2017)
K. Hasegawa, M. Oya, M. Yanagisawa, N. Togawa, Hardware Trojans classification for gate-level netlists based on machine learning, in IEEE 22nd International Symposium on On-Line Testing and Robust System Design (IOLTS), Sant Feliu de Guixols (2016), pp. 203–206
K. Hasegawa, Y. Shi, N. Togawa, Hardware Trojan detection utilizing machine learning approaches, in Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 (2018), pp. 1891–1896
Y. Huang, S. Bhunia, P. Mishra, MERS: statistical test generation for side-channel analysis based Trojan detection, in ACM Conference on Computer and Communications Security (2016)
Y. Huang, S. Bhunia, P. Mishra, Scalable test generation for Trojan detection using side channel analysis, in IEEE Transactions on Information Forensics and Security13(11), 2746–2760 (Nov. 2018)
T. Inoue, K. Hasegawa, Y. Kobayashi, M. Yanagisawa, N. Togawa, Designing subspecies of hardware Trojans and their detection using neural network approach, in IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin (2018)
Y. Jin, Y. Makris, Hardware Trojan detection using path delay fingerprint, in IEEE International Workshop on Hardware-Oriented Security and Trust (2008), pp. 5157
Y. Jin, Y. Makris, Hardware Trojans in wireless cryptographic ICs, in IEEE Design and Test of Computers, 27(1), 2635 (2010)
N. Karimian, F. Tehranipoor, D. Forte, Md.T. Rahman, Genetic algorithm for hardware Trojan detection with Ring Oscillator Network (RON), in IEEE International Conference on Technologies for Homeland Security (2015)
S. Kelly, X. Zhang, M. Tehranipoor, A. Ferraiuolo, Detecting hardware Trojans using on-chip sensors in an ASIC design. J. Electron. Test. 31(1), 11–26 (2015)
A. Kulkarni, Y. Pino, T. Mohsenin, SVM-based real-time hardware Trojan detection for many-core platform, in 17th International Symposium on Quality Electronic Design (ISQED), March (2016)
Y. Liu, K. Huang, Y. Makris, Hardware Trojan detection through golden chip-free statistical side-channel fingerprinting, in Proceedings of the 51st Annual Design Automation Conference (DAC ’14). ACM, New York, Article 155 (2014), p. 6
Y. Lyu, P. Mishra, A survey of side channel attacks on caches and countermeasures. J. Hardw. Syst. Secur. 2, 33–50 (2018)
Y. Lyu, P. Mishra, Efficient test generation for Trojan detection using side channel analysis, in Design Automation and Test in Europe (DATE), Florence, Italy, March 25–29 (2019)
M. Mitchell, An Introduction to Genetic Algorithms (MIT Press, Cambridge, 1996)
M. Mohri, A. Rostamizadeh, A. Talwalkar, Foundations of Machine Learning (The MIT Press, Cambridge, 2012). ISBN 9780262018258
S. Narasimhan, D. Du, R. Chakraborty, S. Paul, F. Wolff, C. Papachristou, K. Roy, S. Bhunia, Multiple-parameter side-channel analysis: a non-invasive hardware Trojan detection approach, in IEEE International Symposium on Hardware-Oriented Security and Trust (2010), pp. 1318
M. Oya, Y. Shi, M. Yanagisawa, N. Togawa, A score-based classification method for identifying hardware-Trojans at gate-level netlists, in Proceedings of the 2015 Design, Automation and Test in Europe (DATE ’15). EDA Consortium, San Jose (2015), pp. 465–470
E.M. Rudnick et al., A genetic algorithm framework for test generation. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 16, 1034–1044 (1997)
S. Saha et al., Improved test pattern generation for hardware Trojan detection using genetic algorithm and boolean satisfiability, in Cryptographic Hardware and Embedded Systems – CHES (2015)
A. Waksman, M. Suozzo, S. Sethumadhavan, Fanci: identification of stealthy malicious logic using boolean functional analysis, in ACM SIGSAC Conference on Computer & Communications Security (2013), pp. 697–708
F. Wolff et al., Towards Trojan-free trusted ICs: problem analysis and detection scheme, in Design, Automation and Test in Europe (2008)
K. Worley, Md. T. Rahman, Supervised machine learning techniques for Trojan detection with ring oscillator network (2019). Available at arXiv:1903.04677v1
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Farahmandi, F., Huang, Y., Mishra, P. (2020). Trojan Detection Using Machine Learning. In: System-on-Chip Security. Springer, Cham. https://doi.org/10.1007/978-3-030-30596-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-30596-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30595-6
Online ISBN: 978-3-030-30596-3
eBook Packages: EngineeringEngineering (R0)