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
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
Tehranipoor, M., Koushanfar, F.: A survey of hardware Trojan taxonomy and detection. IEEE Design Test Comput. 27(1), 10–25 (2010)
Bhunia, S., Hsiao, M.S., Banga, M., Narasimhan, S.: Hardware Trojan attacks: threat analysis and countermeasures. Proc. IEEE 102(8), 1229–1247 (2014)
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)
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)
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)
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)
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)
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_31
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nandhini, A.P., Sai Bhavani, M., Dharani Dharan, S., Harish, N., Priyatharishini, M. (2019). Effective Hardware Trojan Detection Using Front-End Compressive Sensing. In: Thampi, S., Madria, S., Wang, G., Rawat, D., Alcaraz Calero, J. (eds) Security in Computing and Communications. SSCC 2018. Communications in Computer and Information Science, vol 969. Springer, Singapore. https://doi.org/10.1007/978-981-13-5826-5_20
Download citation
DOI: https://doi.org/10.1007/978-981-13-5826-5_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5825-8
Online ISBN: 978-981-13-5826-5
eBook Packages: Computer ScienceComputer Science (R0)