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Effective Hardware Trojan Detection Using Front-End Compressive Sensing

  • A. P. NandhiniEmail author
  • M. Sai Bhavani
  • S. Dharani Dharan
  • N. Harish
  • M. Priyatharishini
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 969)

Abstract

Hardware Trojans (HT) have become a serious security concern for semiconductor industries due to the rise in outsourcing of the fabrication of ICs. Fabrication foundries with minimal security pave an easy way for the adversary to tamper the IC design and place a malicious circuit of his interest into the IC. Detection of these malicious circuits is also becoming increasingly difficult due to a large variety of trojans, increase in their complexity and their stealthy nature. An effective Hardware Trojan detection algorithm using a signal processing technique called Compressive Sensing (CS) is proposed in this work. This method mainly focuses on test vector reduction which greatly reduces the test time during the detection process. This compression of test vectors is done using Compressive Sensing (CS). Key nodes in the selected ISCAS ’85 circuits are identified by computing Transition Probability (TP) of each node in the circuit. The circuits with trojans inserted in the key nodes are subjected to further non-destructive analyses to detect the presence of trojan(s). Also, metrics such as True Positive Rate (TPR) and Probability of Detection (PD) are validated to analyse the efficiency of the proposed algorithm

Keywords

Hardware Trojan Test vector reduction Key node identification Transition probability Compressive Sensing 

References

  1. 1.
    Tehranipoor, M., Koushanfar, F.: A survey of hardware Trojan taxonomy and detection. IEEE Design Test Comput. 27(1), 10–25 (2010)CrossRefGoogle Scholar
  2. 2.
    Bhunia, S., Hsiao, M.S., Banga, M., Narasimhan, S.: Hardware Trojan attacks: threat analysis and countermeasures. Proc. IEEE 102(8), 1229–1247 (2014)CrossRefGoogle Scholar
  3. 3.
    Wang, X., Tehranipoor, M., Plusquellic, J.: Detecting malicious inclusions in secure hardware: challenges and solutions. In: 2008 IEEE International Workshop on Hardware-Oriented Security and Trust, Anaheim, CA, pp. 15–19 (2008)Google Scholar
  4. 4.
    Foucart, S., Rauhut, H.: An invitation to compressive sensing. In: A Mathematical Introduction to Compressive Sensing. Applied and Numerical Harmonic Analysis, pp. 1–39. Birkhäuser, New York (2013)CrossRefGoogle Scholar
  5. 5.
    Karunakaran, D.K., Mohankumar, N.: Malicious combinational Hardware Trojan detection by Gate Level Characterization in 90nm technology. In: Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Hefei, pp. 1–7 (2014)Google Scholar
  6. 6.
    Popat, J., Mehta, U.: Transition probabilistic approach for detection and diagnosis of Hardware Trojan in combinational circuits. In: 2016 IEEE Annual India Conference (INDICON), Bangalore, pp. 1–6 (2016)Google Scholar
  7. 7.
    Qaisar, S., Bilal, R.M., Iqbal, W., Naureen, M., Lee, S.: Compressive sensing: from theory to applications, a survey. J. Commun. Networks 15(5), 443–456 (2013)CrossRefGoogle Scholar
  8. 8.
    Atchuta Sashank, K., Reddy, H.S., Pavithran, P., Akash, M.S., Nirmala Devi, M.: Hardware Trojan detection using effective test patterns and selective segmentation. In: Thampi, S.M., Martínez Pérez, G., Westphall, C.B., Hu, J., Fan, C.I., Gómez Mármol, F. (eds.) SSCC 2017. CCIS, vol. 746, pp. 379–386. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-6898-0_31CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • A. P. Nandhini
    • 1
    Email author
  • M. Sai Bhavani
    • 1
  • S. Dharani Dharan
    • 1
  • N. Harish
    • 1
  • M. Priyatharishini
    • 1
  1. 1.Department of Electronics and Communication EngineeringAmrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita UniversityCoimbatoreIndia

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