A Hardware Trojan Detection Method Based on Compression Marginal Fisher Analysis
-
摘要: 针对物理环境下旁路分析技术对电路中规模较小的硬件木马检出率低的问题，该文引入边界Fisher分析(MFA)方法，并提出一种基于压缩边界Fisher分析(CMFA)的硬件木马检测方法。通过减小样本的同类近邻样本与该样本以及类中心之间距离和增大类中心的同类近邻样本与异类样本之间距离的方式，构建投影空间，发现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，该方法具有比已有检测方法更高的检测精度，能够检测出占原始电路规模0.04%的硬件木马。
-
关键词:
- 硬件木马检测 /
- 集成电路 /
- 旁路分析 /
- 流形学习 /
- 边界Fisher分析
Abstract: Against the problem of low detection rate to detect small hardware Trojan by side-channel in physical environment, the Marginal Fisher Analysis (MFA) is introduced. On the basis, a hardware Trojan detection method based on Compression Marginal Fisher Analysis (CMFA) is proposed. The projection space is constructed by reducing the distance between the sample and its same neighbor samples, and the distance between the same neighbor samples and the center of the same kind, and increasing the distance between the same neighbor samples of the center and the sample in different kind. Thus, the difference in the original data is found without any assumptions about data distribution, and the detection of hardware Trojan is achieved. The hardware Trojan detection experiment in AES encryption circuit shows that this method can effectively distinguish the statistical difference in side-channel signal between reference chip and Trojan chip and detect the hardware Trojan whose scale is 0.04% of the original circuit. -
DOFE J, FREY J, and YU Qiaoyu. Hardware security assurance in emerging IoT applications[C]. 2016 IEEE International Symposium on Circuits and Systems, Montreal, Canada, 2016: 2050–2053. SUMATHI G, SRIVANI L, MURTHY D T, et al. A review on HT attacks in PLD and ASIC designs with potential defence solutions[J]. IETE Technical Review, 2018, 35(1): 64–77. doi: 10.1080/02564602.2016.1246385 CHAKRABORTY R S, WOLFF F, PAUL S, et al. MERO: A statistical approach for hardware Trojan detection[C]. The 11th International Workshop on Cryptographic Hardware and Embedded Systems, Switzerland, 2009: 396–410. SAHA S, CHAKRABORTY R S, NUTHAKKI S S, et al. Improved test pattern generation for hardware Trojan detection using genetic algorithm and Boolean satisfiability[C]. The 17th International Workshop on Cryptographic Hardware and Embedded Systems, Saint-Malo, France, 2015: 577–596. LESPERANCE N, KULKARNI S, CHENG K T, et al. Hardware Trojan detection using exhaustive testing of k-bit subspaces[C]. The 20th Asia and South Pacific Design Automation Conference, Chiba, Japan, 2015: 755–760. XUE Mingfu, HU Aiqun, and LI Guyue. Detecting hardware Trojan through heuristic partition and activity driven test pattern generation[C]. 2014 Communications Security Conference, Beijing, China, 2014: 1–6. AGRAWAL D, BAKTIR S, KARAKOYUNLU D, et al. Trojan detection using IC fingerprinting[C]. 2007 IEEE Symposium on Security and Privacy, Berkeley, USA, 2007: 296–310. 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 XIAO Kan, ZHANG Xuehui, and TEHRANIPOOR M. A clock sweeping technique for detecting hardware trojans impacting circuits delay[J]. IEEE Design & Test, 2013, 30(2): 26–34. doi: 10.1109/MDAT.2013.2249555 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. doi: 10.11897/SP.J.1016.2018.00439XUE Mingfu, WANG Jian, and HU Aiqun. Adaptive optimization of two-class classification-based hardware Trojan detection method[J]. Chinese Journal of Computers, 2018, 41(2): 439–451. doi: 10.11897/SP.J.1016.2018.00439 骆扬, 王亚楠. 物理型硬件木马失效机理及检测方法[J]. 物理学报, 2016, 65(11): 110602. doi: 10.7498/APS.65.110602LUO Yang and WANG Yanan. Physical hardware trojan failure analysis and detection method[J]. Acta Physica Sinica, 2016, 65(11): 110602. doi: 10.7498/APS.65.110602 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. doi: 10.3969/J.ISSN.1000-436x.2013.04.014ZHANG Peng, WANG Xincheng, and ZHOU Qing. Hardware Trojans detection based on projection pursuit[J]. Journal on Communications, 2013, 34(4): 122–126. doi: 10.3969/J.ISSN.1000-436x.2013.04.014 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. doi: 10.3969/J.ISSN.0372-2112.2017.03.023LI Xiongwei, WANG Xiaohan, 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 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. doi: 10.13245/J.HUST.160211ZHAO Yiqiang, LIU Shenfeng, HE Jiaji, 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. doi: 10.13245/J.HUST.160211 YAN Shuicheng, XU Dong, ZHANG Benyu, 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 何进荣, 丁立新, 崔梦天, 等. 基于矩阵指数变换的边界Fisher分析[J]. 计算机学报, 2014, 37(10): 2196–2205.HE Jinrong, DING Lixin, CUI Mengtian, et al. Marginal Fisher analysis based on matrix exponential transformation[J]. Chinese Journal of Computers, 2014, 37(10): 2196–2205. 李艳霞, 柴毅, 胡友强, 等. 不平衡数据分类方法综述[J]. 控制与决策, 2019, 34(4): 673–688. doi: 10.13195/J.KZYJC.2018.0865LI Yanxia, CHAI Yi, HU Youqiang, et al. Review of imbalanced data classification methods[J]. Control and Decision, 2019, 34(4): 673–688. doi: 10.13195/J.KZYJC.2018.0865 -
下载:
图(7)
计量
- 文章访问数: 2206
- HTML全文浏览量: 872
- PDF下载量: 42
- 被引次数: 0
下载:
