Hardware Trojan Detection Based on Multiple Structural Features

Yingjian YAN; Conghui ZHAO; Yanjiang LIU

doi:10.11999/JEIT210003

Volume 43 Issue 8

Aug. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(8): 2128-2139

Yingjian YAN, Conghui ZHAO, Yanjiang LIU. Hardware Trojan Detection Based on Multiple Structural Features[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2128-2139. doi: 10.11999/JEIT210003

Citation:

Yingjian YAN, Conghui ZHAO, Yanjiang LIU. Hardware Trojan Detection Based on Multiple Structural Features[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2128-2139. doi: 10.11999/JEIT210003

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Hardware Trojan Detection Based on Multiple Structural Features

doi: 10.11999/JEIT210003

Strtegic Support Force Information Engineering University, Zhengzhou 450000, China

Received Date: 2021-01-04
Rev Recd Date: 2021-03-10

Available Online: 2021-06-24

Publish Date: 2021-08-10

Abstract

Abstract

Hardware Trojans are the main security threats of the third-party Intellectual Property (IP) cores. The existing pre-silicon hardware Trojan detection methods are difficult to be used in a large amount of hardware Trojans detection and the detection accuracy is hard to be enhanced. A gate-level netlist abstract modeling algorithm is proposed to reduce the cost of trustworthiness analysis method, which establishes a directed graph of the gate-level netlist and stores the graph data into the crosslinked list. Furthermore, the characteristics of hardware Trojans are analyzed in the view of the attacker view and a 7-dimensional feature vector based on the directed graph is proposed. Moreover, a hardware Trojan feature extraction algorithm is proposed to extract the 7-dimensional feature of the gate-level netlist, and a Trojan feature expansion algorithm based on the Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTEENN) is introduced to expand the number of Trojan samples and the Support Vector Machine (SVM) algorithm is utilized to identify the existence of hardware Trojan. 15 benchmark circuits from the Trust-hub are used to validate the efficacy of the proposed approach and the accuracy rate we achieved is 97.02%. True Positive Rate (TPR) is increased by 13.80%, True Negative Rate (TNR) and ACCuracy (ACC) is increased by 0.92% and 2.48% respectively compared with the existing reference.
- Hardware Trojan detection,
- Intellectual Property (IP) core,
- Directed graph,
- Structural feature,
- Support Vector Machine (SVM)

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References(17)

References

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