Abstract
Due to the globalization, advanced information and simplicity of computerized frameworks have left the substance of the advanced media greatly unreliable. Security concerns, particularly for integrated circuits (ICs) and systems utilized as a part of critical applications and cyber infrastructure have been encountered due to Hardware Trojan. In last decade Hardware Trojans have been investigated significantly by the research community and proposed solution using either test time analysis or run time analysis. Test time analysis uses a reverse engineering based approach to detect Trojan which, limits to the destruction of ICs in detection process.
This paper explores Hardware Trojans from the most recent decade and endeavors to catch the lessons learned to detect Hardware Trojan and proposed an innovative and powerful reverse engineering based Hardware Trojan detection method using Support Vector Machine (SVM). SVM uses benchmark golden ICs for training purpose and use them for the future detection of Trojan infected ICs. Simulation process of proposed method was carried out by utilizing state-of-art tools on openly accessible benchmark circuits ISCAS 85 and ISCAS 89 and demonstrates Hardware Trojans detection accuracy using SVM over different kernel functions. The results show that Radial kernel based SVM performs better among linear and polynomial.
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References
Karri, R., Rajendran, J., Rosenfeld, K.: Trojan taxonomy. In: Tehranipoor, M., Wang, C. (eds.) Introduction to Hardware Security and Trust, pp. 325–338. Springer, New York (2012)
Zhang, X., Tehranipoor, M.: Case study: detecting hardware Trojans in third-party digital IP cores. In: 2011 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST), San Diego, CA, USA, 5–6 June 2011 (2011)
Jin, Y., Makris, Y.: Hardware Trojans in wireless cryptographic integrated circuits. IEEE Des. Test PP(99), 1 (2009)
Narasimhan, S., Bhunia, S.: Hardware Trojan detection. In: Introduction to Hardware Security and Trust, pp. 339–364. Springer, New York (2012)
Tehranipoor, M., Wang, C.: Introduction to Hardware Security and Trust. Springer, New York (2012)
Tehranipoor, M., Koushanfar, F.: A survey of hardware Trojan taxonomy and detection. IEEE Des. Test 27(1), 10–25 (2010)
Shiyanovskii, Y., Wolff, F., Rajendran, A., Papachristou, C., Weyer, D., Clay, W.: Process reliability based Trojans through NBTI and HCI effects. In: 2010 NASA/ESA Conference Adaptive Hardware and Systems (AHS), pp. 215–222. IEEE (2010)
Bao, C., Forte, D., Srivastava, A.: On application of one-class SVM to reverse engineering-based hardware Trojan detection. In: 15th International Symposium on Quality Electronic Design (2014)
Reverse Engineering. Techopedia Inc. https://www.techopedia.com/definition/3868/reverse-engineering
Torrance, R., James, D.: The state-of-the-art in semiconductor reverse engineering. In: DAC, pp. 333–338 (2011)
Chakraborty, R., Wolff, F., Paul, S., Bhunia, S.: MERO: a statistical approach for hardware Trojan detection. In: Clavier, C., Gaj, K. (eds.) Cryptographic Hardware and Embedded Systems - CHES 2009. Lecture Notes in Computer Science, vol. 5747, pp. 396–410. Springer, Heidelberg (2009)
Narasimhan, S., Du, D., Chakraborty, R.S., Paul, S., Wolff, F., Papachristou, C., Roy, K., Bhunia, S.: Multiple-parameter side-channel analysis: a non-invasive hardware Trojan detection approach. In: 2010 IEEE International Symposium Hardware-Oriented Security and Trust (HOST), pp. 13–18 (2010)
Wolff, F., Papachristou, C., Bhunia, S., Chakraborty, R.S.: Towards Trojan-free trusted ICs: problem analysis and detection scheme. In: Proceedings of the Conference on Design, Automation and Test in Europe, Munich, Germany (2008)
Perceptive Analytics: Machine Learning Using Support Vector Machines. R-Bloggers, 19 April 2007. https://www.r-bloggers.com/machine-learning-using-support-vector-machines/
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification. In: National Taiwan University, Taipei 106, Taiwan (2016)
Jenihhin, M.: Benchmark circuits, 2 January 2007. http://www.pld.ttu.ee/~maksim/benchmarks/
Precision and recall (2017). https://en.wikipedia.org/wiki/Precision_and_recall
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Jain, G., Raghuwanshi, S., Vishwakarma, G. (2018). Hardware Trojan: Malware Detection Using Reverse Engineering and SVM. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_51
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DOI: https://doi.org/10.1007/978-3-319-76348-4_51
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