Hardware Trojan Detection Based on Path Feature and Support Vector Machine

FENG Yan; CHEN Lan

doi:10.11999/JEIT220500

Volume 45 Issue 6

Jun. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(6): 1921-1932

FENG Yan, CHEN Lan. Hardware Trojan Detection Based on Path Feature and Support Vector Machine[J]. Journal of Electronics & Information Technology, 2023, 45(6): 1921-1932. doi: 10.11999/JEIT220500

Citation:

FENG Yan, CHEN Lan. Hardware Trojan Detection Based on Path Feature and Support Vector Machine[J]. Journal of Electronics & Information Technology, 2023, 45(6): 1921-1932. doi: 10.11999/JEIT220500

FENG Yan, CHEN Lan. Hardware Trojan Detection Based on Path Feature and Support Vector Machine[J]. Journal of Electronics & Information Technology, 2023, 45(6): 1921-1932. doi: 10.11999/JEIT220500

Citation:

FENG Yan, CHEN Lan. Hardware Trojan Detection Based on Path Feature and Support Vector Machine[J]. Journal of Electronics & Information Technology, 2023, 45(6): 1921-1932. doi: 10.11999/JEIT220500

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Hardware Trojan Detection Based on Path Feature and Support Vector Machine

doi: 10.11999/JEIT220500

FENG Yan,
CHEN Lan^,

Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China

Received Date: 2022-04-22
Rev Recd Date: 2022-09-22

Available Online: 2022-09-29

Publish Date: 2023-06-10

Abstract

Abstract

Hardware Trojan attack has become a serious threat to Integrated Circuit(IC). Hardware Trojans are hidden, rare triggered and the data-sets of Trojan benchmarks are unbalanced, a hardware Trojan detection method that performs a static analysis in gate-level netlist is presented. The path-feature based on the principle of design-for-test is proposed to simplify the analysis of feature. Based on the path-feature extracted in a circuit, the nets are classified into two groups with the Support Vector Machine (SVM) machine learning. It uses the double-weighting method of training-set to improve the performance of the classifier. Experimental results demonstrate that this method can be used to detect the suspicious nets in circuits and the ACCuracy (ACC) can achieve up to 99.85%.The static weighting method improves the performance of the classifier and the improvement of accuracy can achieve up 5.58%. Compared with the existing reference, the size of feature is only 36%, True Positive Rate (TPR) is decreased by 1.07%, True Negative Rate (TNR) is increased by 2.74% and ACC is increased by 2.92% respectively. This work verifies the efficiency of path-feature and SVM machine learning for Hardware Trojan detection and clarifies the relationship between the balance of data-sets and the detection performance.
- Hardware Trojan,
- Path feature,
- Support Vector Machine (SVM),
- Static weighting

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References(22)

References

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