Hardware Trojan detection based on improved marginal Fisher analysis nearest neighbor selection

doi:10.3785/j.issn.1008-973X.2020.01.018

Journal of ZheJiang University (Engineering Science)

2020, Vol. 54

Issue (1): 152-159 DOI: 10.3785/j.issn.1008-973X.2020.01.018

Computer Technology, Information Engineering

Hardware Trojan detection based on improved marginal Fisher analysis nearest neighbor selection

Xiao-han WANG(

),Tao WANG*(

),Yang ZHANG,Guang-kai LIU

Equipment Simulation Training Center, Army Engineering University Shijiazhuang Campus, Shijiazhuang 050003, China

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Abstract

A hardware Trojan detection method based on marginal Fisher analysis was proposed aiming at the problem of low detection accuracy for small-scale hardware Trojan by side-channel analysis. The rule was defined to select the nearest neighbor samples. Then the projection subspace was constructed by reducing the distance between the samples and their nearest neighbor samples in the same class and increasing the distance between the samples and their nearest neighbor samples in different class. The difference features in the original side-channel signals was found without any assumptions about data distribution, and the detection of hardware Trojan was achieved. The hardware Trojan detection experiment in AES encryption circuit shows that the hardware Trojan whose scale is 0.02% of the original circuit can be detected by the method. The method is better than existing detection methods.

Key words： integrated circuit side-channel analysis hardware Trojan detection manifold learning marginal Fisher analysis

Received: 06 March 2019 Published: 05 January 2020

CLC:

TN 918

Corresponding Authors: Tao WANG E-mail: wxh2225@126.com;T_Wang_mail@163.com

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Cite this article:

Xiao-han WANG,Tao WANG,Yang ZHANG,Guang-kai LIU. Hardware Trojan detection based on improved marginal Fisher analysis nearest neighbor selection. Journal of ZheJiang University (Engineering Science), 2020, 54(1): 152-159.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.01.018 OR http://www.zjujournals.com/eng/Y2020/V54/I1/152

改进边界Fisher分析近邻选择的硬件木马检测

针对旁路分析技术对小规模硬件木马检测精度低的问题，提出基于边界Fisher分析的硬件木马检测方法.定义规则式选择近邻样本，以减小样本与其同类近邻样本间距离和增大样本与其异类近邻样本间距离的方式构建投影子空间，在不对数据分布作任何假设的前提下，提取原始功耗旁路信号中的差异特征，实现硬件木马检测. AES加密电路中的硬件木马检测实验表明，该方法能够检测出占原始电路规模0.02%的硬件木马，优于已有的检测方法.

关键词： 集成电路, 旁路分析, 硬件木马检测, 流形学习, 边界Fisher分析

Fig.1 MFA method analysis

Fig.2 Motion trend of samples after improvement

Fig.3 Detection results of Trojan 1 using K-L transform, kernel MMC, MFA and improved MFA

Fig.4 Detection results of Trojan 5 using K-L transform, kernel MMC, MFA and improved MFA

Fig.5 Detection results comparison of different scale hardware Trojans

Fig.6 Detection results of 10 chips to be tested

Fig.7 Detection results of first group of experiments using improved MFA


[1]	DOFE J, FREY J, YU Q. Hardware security assurance in emerging IoT applications [C]// Proceedings of IEEE International Symposium on Circuits and Systems. Montreal: IEEE, 2016: 2050-2053.

[2]	BHUNIA S, ABRAMOVICI M, AGRAWAL D, et al Protection against hardware Trojan attacks: towards a comprehensive solution[J]. IEEE Design Test Computer, 2013, 30 (3): 6- 17 doi: 10.1109/MDT.2012.2196252

[3]	WANG X, TEHRANIPOOR M, PLUSQUELLIC J, et al. Detecting malicious inclusions in secure hardware: challenges and solutions [C]// Proceedings of IEEE International Workshop on Hardware-Oriented Security and Trust. Anaheim: IEEE, 2008: 15-19.

[4]	WOLFF F, PAPACHRISTOU C, BHUNIA S, et al. Towards Trojan free trusted ICs: problem analysis and detection scheme [C]// Proceedings of Design, Automation and Test in Europe. Munich: IEEE, 2008: 1362-1365.

[5]	CHAKRABORTY R S, NARASIMHAN S, BHUNIA S, et al. Hardware Trojan: threats and emerging solutions [C]// Proceedings of IEEE International Workshop on High Level Design Validation and Test Workshop. San Francisco: IEEE, 2009: 166-171.

[6]	AGRAWAL D, BAKTIR S, KARAKOYUNLU D, et al. Trojan detection using IC fingerprinting [C]// Proceedings of IEEE Symposium on Security and Privacy. Berkeley: IEEE, 2007: 296-310.

[7]	BALASCH J, GIERLICHS B, VERBAUWHEDE I. Electromagnetic circuit fingerprints for Hardware Trojan detection [C]// Proceedings of IEEE International Symposium on Electromagnetic Compatibility. Dresden: IEEE, 2015: 246-251.

[8]	XIAO K, ZHANG X H, TEHRANIPOOR M A clock sweeping technique for detecting hardware trojans impacting circuits delay[J]. IEEE Design and Test of Computers, 2013, 30 (2): 26- 34 doi: 10.1109/MDAT.2013.2249555

[9]	NOWROZ A N, HU K, KOUSHANFAR F, et al Novel techniques for high-sensitivity hardware Trojan detection using thermal and power maps[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2014, 33 (12): 1792- 1805 doi: 10.1109/TCAD.2014.2354293

[10]	STELLARI F, SONG P, WEGER A J, et al. Verification of untrusted chips using trusted layout and emission measurements [C]// Proceedings of IEEE International Symposium on Hardware-Oriented Security and Trust. Arlington: IEEE, 2014: 19-24.

[11]	薛明富, 王箭, 胡爱群自适应优化的二元分类型硬件木马检测方法[J]. 计算机学报, 2018, 41 (2): 439- 451 XUE Ming-fu, WANG Jian, HU Ai-qun Adaptive optimization of two-class classification-based hardware Trojan detection method[J]. Journal of Computers, 2018, 41 (2): 439- 451 doi: 10.11897/SP.J.1016.2018.00439

[12]	NARASIMHAN S, WANG X, DU D, et al. TeSR: a robust temporal self-referencing approach for hardware Trojan detection [C]// Proceedings of IEEE International Symposium on Hardware-Oriented Security and Trust. San Diego: IEEE, 2011: 71-74.

[13]	BANGA M, MICHAEL S H. A novel sustained vector technique for the detection of hardware Trojans [C]// Proceedings of IEEE International Conference on VLSI Design. New Delhi: IEEE, 2009: 327–332.

[14]	张鹏, 王新成, 周庆基于投影寻踪分析的芯片硬件木马检测[J]. 通信学报, 2013, 34 (4): 122- 126 ZHANG Peng, WANG Xin-cheng, ZHOU Qing Hardware Trojans detection based on projection pursuit[J]. Journal on Communications, 2013, 34 (4): 122- 126

[15]	李雄伟, 王晓晗, 张阳, 等一种基于核最大间距准则的硬件木马检测新方法[J]. 电子学报, 2017, 45 (3): 656- 661 LI Xiong-wei, WANG Xiao-han, ZHANG Yang, et al A new hardware Trojan detection method based on kernel maximum margin criterion[J]. Acta Electronica Sinica, 2017, 45 (3): 656- 661 doi: 10.3969/j.issn.0372-2112.2017.03.023

[16]	赵毅强, 刘沈丰, 何家骥, 等基于自组织竞争神经网络的硬件木马检测方法[J]. 华中科技大学学报: 自然科学版, 2016, 44 (2): 51- 55 ZHAO Yi-qiang, LIU Shen-feng, HE Jia-ji, et al Hardware Trojan detection technology based on self-organizing competition neural network[J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2016, 44 (2): 51- 55

[17]	YAN S, XU D, ZHANG B, et al Graph embedding and extensions: a general framework for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29 (1): 40- 51 doi: 10.1109/TPAMI.2007.250598

[18]	何进荣, 丁立新, 李照奎, 等基于边界判别投影的数据降维[J]. 软件学报, 2014, 25 (4): 826- 838 HE Jin-rong, DING Li-xin, LI Zhao-kui, et al Margin discriminant projection for dimensionality reduction[J]. Journal of Software, 2014, 25 (4): 826- 838

[19]	毛莎莎. 基于贪婪优化和投影变换的集成分类器算法研究[D]. 西安: 西安电子科技大学, 2014. MAO Sha-sha. A study of classifier ensemble methods based on greedy optimization and projection transformation [D]. Xi'an: Xi'an University of Electronic Science and Technology, 2014.

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