Hybrid Multi-level Hardware Trojan Detection Method for Gate-level Netlists Based on XGBoost

Ying ZHANG; Shen LI; Xin CHEN; Jiaqi YAO; Zhiming MAO

doi:10.11999/JEIT200874

Volume 43 Issue 10

Oct. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(10): 3050-3057

Ying ZHANG, Shen LI, Xin CHEN, Jiaqi YAO, Zhiming MAO. Hybrid Multi-level Hardware Trojan Detection Method for Gate-level Netlists Based on XGBoost[J]. Journal of Electronics & Information Technology, 2021, 43(10): 3050-3057. doi: 10.11999/JEIT200874

Citation:

Ying ZHANG, Shen LI, Xin CHEN, Jiaqi YAO, Zhiming MAO. Hybrid Multi-level Hardware Trojan Detection Method for Gate-level Netlists Based on XGBoost[J]. Journal of Electronics & Information Technology, 2021, 43(10): 3050-3057. doi: 10.11999/JEIT200874

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Hybrid Multi-level Hardware Trojan Detection Method for Gate-level Netlists Based on XGBoost

doi: 10.11999/JEIT200874

College of Electronics and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Funds: The National Natural Science Foundation of China (61701228, 61106029), The Science and Technology on Analog Integrated Circuit Laboratory (61428020304), The AeronauticalScience Foundation of China (20180852005)

Received Date: 2020-10-12
Rev Recd Date: 2021-07-20

Available Online: 2021-07-30

Publish Date: 2021-10-18

Abstract

Abstract

A hybrid multi-level hardware Trojan detection method based on XGBoost algorithm is proposed for the problem of hardware Trojans implanted by malicious third-party manufacturers. The detection method treats each wire in gate-level netlist as a node and detects Trojans in three levels. Firstly, the effective static features of the circuit are extracted and the XGBoost algorithm is applied to detect the suspicious Trojan circuits. Common circuits distinguished at the first level continued to be detected at the second level by analyzing scan chain structural features. Finally, dynamic detection is used to increase further the accuracy of Trojans detection. Experimental results on Trust-hub benchmark show that this method has a higher accuracy compared with other existing detection methods. This detection method can finally achieve 94.0% average True Positive Rate (TPR) and 99.3% average True Negative Rate (TNR).
- Hardware Trojan detection,
- XGBoost,
- Gate-level netlists,
- Static detection,
- Dynamic detection

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References(20)

References

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