| Citation: | Xin TONG, Ying LI, Lan CHEN. Application of SVM Machine Learning to Hardware Trojan Detection Using Side-channel Analysis[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1643-1651. doi: 10.11999/JEIT190532
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Integrated Circuits (ICs) are suffering severer threats caused by Hardware Trojans (HTs), some of which hide in routine operations by coercing firmware or hardware. Along with conventional side-channel detection not always getting golden-chip, HTs become more difficult to detect. An improved Support Vector Machine (SVM) machine learning frameworks for this is proposed using system-level side-channel analysis. Cross validation experimental results on Field Programmable Gate Array (FPGA) show that in the condition of golden-chip, supervised SVM achieves 85.8% test accuracy in average. After grouping, outlier-removing and normalization, it rises by 4%. Even if golden-chip is out of hand, semi-supervised SVM has accuracy to judge HTs existence, averaging in 52.9%-79.5% under different test modes. Comparing with existing researches, this work verifies the efficiency of SVM for HT detection in instruction level, and points out the relationship between diversified learning conditions with detection performance.
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钟晶鑫, 王建业, 阚保强. 基于温度特征分析的硬件木马检测方法[J]. 电子与信息学报, 2018, 40(3): 743–749. doi: 10.11999/JEIT170443
ZHONG Jingxin, WANG Jianye, and KAN Baoqiang. Hardware Trojan detection through temperature characteristics analysis[J]. Journal of Electronics &Information Technology, 2018, 40(3): 743–749. doi: 10.11999/JEIT170443
|
|
RAD R M, WANG Xiaoxiao, TEHRANIPOOR M, et al. Power supply signal calibration techniques for improving detection resolution to hardware Trojans[C]. 2008 IEEE/ACM International Conference on Computer-Aided Design, San Jose, USA, 2008: 632–639. doi: 10.1109/ICCAD.2008.4681643.
|
|
LAMECH C, AARESTAD J, PLUSQUELLIC J, et al. REBEL and TDC: Two embedded test structures for on-chip measurements of within-die path delay variations[C]. 2011 IEEE/ACM International Conference on Computer-Aided Design, San Jose, USA, 2011: 170–177. doi: 10.1109/ICCAD.2011.6105322.
|
|
DU Dongdong, NARASIMHAN S, CHAKRABORTY R S, et al. Self-referencing: A scalable side-channel approach for hardware Trojan detection[C]. The 12th International Workshop on Cryptographic Hardware and Embedded Systems, Santa Barbara, USA, 2010: 173–187. doi: 10.1007/978-3-642-15031-9_12.
|
|
HE Jiaji, ZHAO Yiqiang, GUO Xiaolong, et al. Hardware Trojan detection through chip-free electromagnetic side-channel statistical analysis[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2017, 25(10): 2939–2948. doi: 10.1109/TVLSI.2017.2727985
|
|
NARASIMHAN S, DU Dongdong, CHAKRABORTY R S, et al. Multiple-parameter side-channel analysis: A non-invasive hardware Trojan detection approach[C]. 2010 IEEE International Symposium on Hardware-Oriented Security and Trust, Anaheim, USA, 2010: 13–18. doi: 10.1109/HST.2010.5513122.
|
|
LIU Yu, JIN Yier, NOSRATINIA A, et al. Silicon demonstration of hardware Trojan design and detection in wireless cryptographic ICs[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2017, 25(4): 1506–1519. doi: 10.1109/TVLSI.2016.2633348
|
|
FORTE D, BAO Chongxi, and SRIVASTAVA A. Temperature tracking: An innovative run-time approach for hardware Trojan detection[C]. 2013 IEEE/ACM International Conference on Computer-Aided Design, San Jose, USA, 2013: 532–539. doi: 10.1109/ICCAD.2013.6691167.
|
|
ZHAO Hong, KWIAT K, KAMHOUA C, et al. Applying chaos theory for runtime hardware Trojan detection[C]. 2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Verona, USA, 2015: 1–6. doi: 10.1109/CISDA.2015.7208642.
|
|
JAP D, HE Wei, and BHASIN S. Supervised and unsupervised machine learning for side-channel based Trojan detection[C]. The 27th IEEE International Conference on Application-specific Systems, Architectures and Processors, London, UK, 2016: 17–24. doi: 10.1109/ASAP.2016.7760768.
|
|
BAO Chongxi, FORTE D, and SRIVASTAVA A. On reverse engineering-based hardware Trojan detection[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2016, 35(1): 49–57. doi: 10.1109/TCAD.2015.2488495
|
|
INOUE T, HASEGAWA K, YANAGISAWA M, et al. Designing hardware Trojans and their detection based on a SVM-based approach[C]. The 12th IEEE International Conference on ASIC, Guiyang, China, 2017: 811–814. doi: 10.1109/ASICON.2017.8252600.
|
|
KULKARNI A, PINO Y, and MOHSENIN T. SVM-based real-time hardware Trojan detection for many-core platform[C]. 2016 17th International Symposium on Quality Electronic Design, Santa Clara, USA, 2016: 362–367. doi: 10.1109/ISQED.2016.7479228.
|
|
LODHI F K, HASAN S R, HASAN O, et al. Power profiling of microcontroller′s instruction set for runtime hardware Trojans detection without golden circuit models[C]. The Design, Automation & Test in Europe Conference & Exhibition, Lausanne, Switzerland, 2017: 294–297. doi: 10.23919/DATE.2017.7927002.
|
|
TEHRANIPOOR M and SALAMANI H. trust-HUB[OL]. https://www.trust-hub.org/, 2018.
|
|
李莹, 周崟灏, 陈岚. 一种旁路检测方法及装置[P]. 中国专利, CN109684881A, 2019.
LI Ying, ZHOU Yinhao, and CHEN Lan. A bypass detection method and device[P]. China patent, CN109684881A, 2019.
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