A Hardware Trojan Detection and Diagnosis Method for Gate-Level Netlists Based on Machine Learning and Graph Theory
The integrated circuit (IC) supply chain has become globalized, thereby inevitably introducing hardware Trojan (HT) threats during the design stage. To safeguard the integrity and security of ICs, many machine learning (ML)-based solutions have been proposed. However, most existing methods lack cons...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/1/59 |
| _version_ | 1797358971542044672 |
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| author | Junjie Wang Guangxi Zhai Hongxu Gao Lihui Xu Xiang Li Zeyu Li Zhao Huang Changjian Xie |
| author_facet | Junjie Wang Guangxi Zhai Hongxu Gao Lihui Xu Xiang Li Zeyu Li Zhao Huang Changjian Xie |
| author_sort | Junjie Wang |
| collection | DOAJ |
| description | The integrated circuit (IC) supply chain has become globalized, thereby inevitably introducing hardware Trojan (HT) threats during the design stage. To safeguard the integrity and security of ICs, many machine learning (ML)-based solutions have been proposed. However, most existing methods lack consideration of the integrity of HTs, thereby resulting in lower true negative rates (TNR) and true positive rate (TPRs). Therefore, to solve these problems, this paper presents a HT detection and diagnosis method for gate-level netlists (GLNs) based on ML and graph theory (GT). In this method, to address the issue of nonuniqueness in submodule partition schemes, the concept of “Maximum Single-Output Submodule (MSOS)” and a partition algorithm are introduced. In addition, to enhance the accuracy of HT diagnosis, we design an implant node search method named breadth-first comparison (BFC). With the support of the aforementioned techniques, we have completed experiments on HT detection and diagnosis. The HT implantation examples selected in this article are sourced from Trust-Hub. The experimental results demostrate the following: (1) The detection method proposed in this article, when detecting gate-level hardware trojans (GLHTs), achieves a TPR exceeding 95%, a TNR exceeding 37%, and F1 values exceeding 97%. Compared to existing methods, this method has improved the TNR for GLHTs by at least 25%. (2) The TPR for diagnosing GLHTs is consistently above 93%, and the TNR is 100%. Compared to existing methods, this method has achieved approximately a 4% improvement in the TPR and a 15% improvement in the TNR for GLHT diagnosis. |
| first_indexed | 2024-03-08T15:09:53Z |
| format | Article |
| id | doaj.art-e6f9f19f87b94279971061ef926d1e71 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T15:09:53Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e6f9f19f87b94279971061ef926d1e712024-01-10T14:54:16ZengMDPI AGElectronics2079-92922023-12-011315910.3390/electronics13010059A Hardware Trojan Detection and Diagnosis Method for Gate-Level Netlists Based on Machine Learning and Graph TheoryJunjie Wang0Guangxi Zhai1Hongxu Gao2Lihui Xu3Xiang Li4Zeyu Li5Zhao Huang6Changjian Xie7CNNC Xi’an Nuclear Instrument Co., Ltd., Xi’an 710061, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaCNNC Xi’an Nuclear Instrument Co., Ltd., Xi’an 710061, ChinaSchool of Decision Sciences, The Hang Seng University of Hong Kong, Hong Kong 999077, ChinaSchool of Computer Science and Technology, North University of China, Taiyuan 030051, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaZhejiang Raina Tech. Inc., Yiwu 322000, ChinaThe integrated circuit (IC) supply chain has become globalized, thereby inevitably introducing hardware Trojan (HT) threats during the design stage. To safeguard the integrity and security of ICs, many machine learning (ML)-based solutions have been proposed. However, most existing methods lack consideration of the integrity of HTs, thereby resulting in lower true negative rates (TNR) and true positive rate (TPRs). Therefore, to solve these problems, this paper presents a HT detection and diagnosis method for gate-level netlists (GLNs) based on ML and graph theory (GT). In this method, to address the issue of nonuniqueness in submodule partition schemes, the concept of “Maximum Single-Output Submodule (MSOS)” and a partition algorithm are introduced. In addition, to enhance the accuracy of HT diagnosis, we design an implant node search method named breadth-first comparison (BFC). With the support of the aforementioned techniques, we have completed experiments on HT detection and diagnosis. The HT implantation examples selected in this article are sourced from Trust-Hub. The experimental results demostrate the following: (1) The detection method proposed in this article, when detecting gate-level hardware trojans (GLHTs), achieves a TPR exceeding 95%, a TNR exceeding 37%, and F1 values exceeding 97%. Compared to existing methods, this method has improved the TNR for GLHTs by at least 25%. (2) The TPR for diagnosing GLHTs is consistently above 93%, and the TNR is 100%. Compared to existing methods, this method has achieved approximately a 4% improvement in the TPR and a 15% improvement in the TNR for GLHT diagnosis.https://www.mdpi.com/2079-9292/13/1/59gate-level hardware trojanmachine learninggraph theorydetection and diagnosis |
| spellingShingle | Junjie Wang Guangxi Zhai Hongxu Gao Lihui Xu Xiang Li Zeyu Li Zhao Huang Changjian Xie A Hardware Trojan Detection and Diagnosis Method for Gate-Level Netlists Based on Machine Learning and Graph Theory Electronics gate-level hardware trojan machine learning graph theory detection and diagnosis |
| title | A Hardware Trojan Detection and Diagnosis Method for Gate-Level Netlists Based on Machine Learning and Graph Theory |
| title_full | A Hardware Trojan Detection and Diagnosis Method for Gate-Level Netlists Based on Machine Learning and Graph Theory |
| title_fullStr | A Hardware Trojan Detection and Diagnosis Method for Gate-Level Netlists Based on Machine Learning and Graph Theory |
| title_full_unstemmed | A Hardware Trojan Detection and Diagnosis Method for Gate-Level Netlists Based on Machine Learning and Graph Theory |
| title_short | A Hardware Trojan Detection and Diagnosis Method for Gate-Level Netlists Based on Machine Learning and Graph Theory |
| title_sort | hardware trojan detection and diagnosis method for gate level netlists based on machine learning and graph theory |
| topic | gate-level hardware trojan machine learning graph theory detection and diagnosis |
| url | https://www.mdpi.com/2079-9292/13/1/59 |
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