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Trojan Localization Using Symbolic Algebra

  • Farimah Farahmandi
  • Yuanwen Huang
  • Prabhat Mishra
Chapter

Abstract

This chapter describes an automated approach to identify untrustworthy components and localize malicious functional modifications. The technique is based on extracting polynomials from gate-level implementation of the untrustworthy component and comparing them with specification polynomials. The proposed approach is applicable when the specification is available. This approach is scalable due to manipulation of polynomials instead of BDD-based analysis used in traditional equivalence checking techniques. Experimental results using Trust-HUB benchmarks demonstrate significant improvement in both localization and test generation efficiency compared to the state-of-the-art Trojan detection techniques.

References

  1. 1.
    J. Aarestad, D. Acharyya, R. Rad, J. Plusquellic, Detecting Trojans through leakage current analysis using multiple supply pad I ddqs, in IEEE Transactions on Information Forensics and Security (IEEE, New York, 2010), pp. 893–904Google Scholar
  2. 2.
    B. Çakir, S. Malik, Hardware Trojan detection for gate-level ICS using signal correlation based clustering, in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition (EDA Consortium, San Jose, 2015), pp. 471–476Google Scholar
  3. 3.
    R.S. Chakraborty, F. Wolf, C. Papachristou, S. Bhunia, MERO: a statistical approach for hardware Trojan detection, in International Workshop on Cryptographic Hardware and Embedded Systems (CHES’09) (Springer, Berlin, 2009), pp. 369–410Google Scholar
  4. 4.
    Formality, User Guide (2007). http://www.vlsiip.com/formality/ug.pdf
  5. 5.
    X. Guo, R.G. Dutta, Y. Jin, F. Farahmandi, P. Mishra, Pre-silicon security verification and validation: a formal perspective, in ACM/IEEE Design Automation Conference (DAC) (ACM, New York, 2015)CrossRefGoogle Scholar
  6. 6.
    M. Hicks, M. Finnicum, S. King, M. Martin, J. Smith, Overcoming an untrusted computing base: Detecting and removing malicious hardware automatically, in IEEE Symposium on Security and Privacy (SP) (IEEE Computer Society, Los Alamitos, 2010), pp. 159–172Google Scholar
  7. 7.
    Y. Huang, S. Bhunia, P. Mishra, MERS: Statistical test generation for side-channel analysis based Trojan detection, in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (ACM, New York, 2016), pp. 130–141Google Scholar
  8. 8.
    Y. Huang, S. Bhunia, P. Mishra, Scalable test generation for Trojan detection using side channel analysis. IEEE Trans. Inf. Forensics Secur. 13(11), 2746–2760 (2018)CrossRefGoogle Scholar
  9. 9.
    Y. Jin, Y. Makris, Hardware Trojan detection using path delay fingerprint, in Hardware-Oriented Security and Trust (HOST) (IEEE, Piscataway, 2008), pp. 51–57Google Scholar
  10. 10.
    Y. Lyu, P. Mishra, A survey of side channel attacks on caches and countermeasures. Springer J. Hardw. Syst. Secur. (HASS) 2(1), 33–50 (2018)CrossRefGoogle Scholar
  11. 11.
    Y. Lyu, P. Mishra, Efficient test generation for Trojan detection using side channel analysis, in Design automation and test in Europe (DATE) (IEEE, Piscataway, 2019)Google Scholar
  12. 12.
    S. Narasimhan, X. Wang, D. Du, R. Chakraborty, S. Bhunia, TeSR: a robust temporal self-referencing approach for hardware Trojan detection, in Hardware-Oriented Security and Trust (HOST) (IEEE, Piscataway, 2011), pp. 71–74Google Scholar
  13. 13.
    M. Oya, Y. Shi, M. Yanagisawa, N. Togawa, A score-based classification method for identifying hardware-trojans at gate-level netlists, in Design Automation and Test in Europe(DATE) (Association for Computing Machinery, New York, 2015), pp. 465–470Google Scholar
  14. 14.
    A. Sayed-Ahmed, D. Gro, M. Soeken, R. Drechsler, et al., Formal verification of integer multipliers by combining gröbner basis with logic reduction, in 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE) (IEEE, Piscataway, 2016), pp. 1048–1053Google Scholar
  15. 15.
    C. Sturton, M. Hicks, D. Wagner, S. King, Defeating UCI: Building stealthy and malicious hardware, in IEEE Symposium on Security and Privacy (SP) (IEEE Computer Society, Los Alamitos, 2011), pp. 64–77Google Scholar
  16. 16.
  17. 17.
  18. 18.
  19. 19.
    A. Waksman, M. Suozzo, S. Sethumadhavan, FANCI: identification of stealthy malicious logic using Boolean functional analysis, in ACM SIGSAC Conference on Computer & Communications Security (ACM, New York, 2013), pp. 697–708Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Farimah Farahmandi
    • 1
  • Yuanwen Huang
    • 2
  • Prabhat Mishra
    • 1
  1. 1.University of FloridaGainesvilleUSA
  2. 2.GoogleMountain ViewUSA

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