A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things
With the development of the Internet of Things, smart devices are widely used. Hardware security is one key issue in the security of the Internet of Things. As the core component of the hardware, the integrated circuit must be taken seriously with its security. The pre-silicon detection methods do n...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719888098 |
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| _version_ | 1832546765464666112 |
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| author | Chen Dong Jinghui Chen Wenzhong Guo Jian Zou |
| author_facet | Chen Dong Jinghui Chen Wenzhong Guo Jian Zou |
| author_sort | Chen Dong |
| collection | DOAJ |
| description | With the development of the Internet of Things, smart devices are widely used. Hardware security is one key issue in the security of the Internet of Things. As the core component of the hardware, the integrated circuit must be taken seriously with its security. The pre-silicon detection methods do not require gold chips, are not affected by process noise, and are suitable for the safe detection of a very large-scale integration. Therefore, more and more researchers are paying attention to the pre-silicon detection method. In this study, we propose a machine-learning-based hardware-Trojan detection method at the gate level. First, we put forward new Trojan-net features. After that, we use the scoring mechanism of the eXtreme Gradient Boosting to set up a new effective feature set of 49 out of 56 features. Finally, the hardware-Trojan classifier was trained and detected based on the new feature set by the eXtreme Gradient Boosting algorithm, respectively. The experimental results show that the proposed method can obtain 89.84% average Recall , 86.75% average F-measure , and 99.83% average Accuracy , which is the best detection result among existing machine-learning-based hardware-Trojan detection methods. |
| format | Article |
| id | doaj-art-c96c441c147b4ee9bcd059989c20d0c0 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-c96c441c147b4ee9bcd059989c20d0c02025-02-03T06:47:19ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-12-011510.1177/1550147719888098A machine-learning-based hardware-Trojan detection approach for chips in the Internet of ThingsChen Dong0Jinghui Chen1Wenzhong Guo2Jian Zou3Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Systems, Fuzhou, ChinaKey Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Systems, Fuzhou, ChinaKey Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Systems, Fuzhou, ChinaKey Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Systems, Fuzhou, ChinaWith the development of the Internet of Things, smart devices are widely used. Hardware security is one key issue in the security of the Internet of Things. As the core component of the hardware, the integrated circuit must be taken seriously with its security. The pre-silicon detection methods do not require gold chips, are not affected by process noise, and are suitable for the safe detection of a very large-scale integration. Therefore, more and more researchers are paying attention to the pre-silicon detection method. In this study, we propose a machine-learning-based hardware-Trojan detection method at the gate level. First, we put forward new Trojan-net features. After that, we use the scoring mechanism of the eXtreme Gradient Boosting to set up a new effective feature set of 49 out of 56 features. Finally, the hardware-Trojan classifier was trained and detected based on the new feature set by the eXtreme Gradient Boosting algorithm, respectively. The experimental results show that the proposed method can obtain 89.84% average Recall , 86.75% average F-measure , and 99.83% average Accuracy , which is the best detection result among existing machine-learning-based hardware-Trojan detection methods.https://doi.org/10.1177/1550147719888098 |
| spellingShingle | Chen Dong Jinghui Chen Wenzhong Guo Jian Zou A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things International Journal of Distributed Sensor Networks |
| title | A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things |
| title_full | A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things |
| title_fullStr | A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things |
| title_full_unstemmed | A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things |
| title_short | A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things |
| title_sort | machine learning based hardware trojan detection approach for chips in the internet of things |
| url | https://doi.org/10.1177/1550147719888098 |
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