Abstract
Internet of Things (IoT) is an upcoming research area in cyber security and since common security measures are not implemented in these devices, they are most vulnerable to cyber attacks. Though IoT devices are vulnerable to both hardware and software malware attacks, the impact of hardware vulnerabilities will be significant since the devices once fabricated cannot be modified or updated. Among the major hardware vulnerabilities, hardware malware also known as hardware trojan (HT) is critical as it can control, modify, disable or monitor the key information in the device. While many techniques have been explored in the literature to detect HT at the gate level netlist, their computational complexity is very high and detection accuracy is relatively low. In order to reduce the computational complexity and improve the detection accuracy, a novel approach is proposed in this work to detect HT in the gate level netlist using Hamming weight model and unsupervised anomaly based detection method. The leakage power supply current signatures for both HT-free and HT-infected circuits are derived from Hamming weights of the random input sequence. The current signatures are then normalized and applied to one class support vector machine which acts as an anomaly detector to identify the HT-infected circuits. The process parameter and environmental noise variations are considered while characterizing the gates for different Hamming weights. The proposed method is evaluated on ISCAS85 C17 benchmark circuit using 16 nm process technology node in HSpice. A detection accuracy of 100% is achieved even when there is a single malicious gate in the HT-infected circuit. Experiments with other benchmark circuits show that the proposed methodology performs well.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chakraborty, R.S., Wolff, F., Paul, S., Papachristou, C., Bhunia, S.: MERO: a statistical approach for hardware Trojan detection. In: Clavier, C., Gaj, K. (eds.) CHES 2009. LNCS, vol. 5747, pp. 396–410. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04138-9_28
Agrawal, D., Baktir, S., Karakoyunlu, D., Rohatgi, P., Sunar, B.: Trojan detection using IC fingerprinting. In: Proceedings of IEEE Symposium on Security Privacy, pp. 296–310, Berkeley (2007)
Banga, M., Hsiao, M.S.: A region based approach for the identification of hardware Trojans. In: Proceedings of IEEE International Workshop Hardware Oriented Security Trust (HOST), pp. 40–47, Anaheim (2008)
Rad, R., Plusquellic, J., Tehranipoor, M.: A sensitivity analysis of power signal methods for detecting hardware Trojans under real process and environmental conditions. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 18(12), 1735–1744 (2010)
Yuan, C., Chang, C.H., Chen, S.: A cluster-based distributed active current sensing circuit for hardware Trojan detection. IEEE Trans. Inf. Forensics Secur. 9(12), 2220–2231 (2014)
Hassan, S.: COTD: reference-free hardware Trojan detection and recovery based on controllability and observability in gate-Level netlist. IEEE Trans. Inf. Forensics Secur. 12(2), 338–350 (2017)
Sheng, W., Potkonjak, M.: Scalable hardware Trojan diagnosis. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 20(6), 1049–1057 (2012)
Chongxi, B., Forte, D., Srivastava, A.: On application of one-class SVM to reverse engineering-based hardware Trojan detection. In: Proceedings of 15th IEEE International Symposium on Quality Electronic Design (ISQED), pp. 47–54, Santa Clara (2014)
Kan, X., Forte, D., Jin, Y., Karri, R., Bhunia, S., Tehranipoor, M.: Hardware Trojans: lessons learned after one decade of research. ACM Trans. Des. Autom. Electron. Syst. (TODAES) 22(1), 1–6 (2016)
Alioto, M., Giancane, L., Scotti, G., Trifiletti, A.: Leakage power analysis attacks: a novel class of attacks to nanometer cryptographic circuits. IEEE Trans. Circuits Syst. 57(2), 355–367 (2010)
Tax, D.: One-class classification: concept-learning in the absence of counter-examples. Ph.D. dissertation, Delft University of Technology (2001)
Scholkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C.: Support vector method for novelty detection. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 582–588 (1999)
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
Saravanan, P., Mehtre, B.M. (2019). A Novel Approach to Detect Hardware Malware Using Hamming Weight Model and One Class Support Vector Machine. In: Rajaram, S., Balamurugan, N., Gracia Nirmala Rani, D., Singh, V. (eds) VLSI Design and Test. VDAT 2018. Communications in Computer and Information Science, vol 892. Springer, Singapore. https://doi.org/10.1007/978-981-13-5950-7_14
Download citation
DOI: https://doi.org/10.1007/978-981-13-5950-7_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5949-1
Online ISBN: 978-981-13-5950-7
eBook Packages: Computer ScienceComputer Science (R0)