Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level
Hardware Trojan refers to a malicious modification of an integrated circuit (IC). To eliminate the complications arising from designing an IC which includes a Trojan, it is suggested to apply Trojan detection as early as at register-transfer level (RTL). In this paper, we propose a hardware Trojan d...
| Main Authors: | Choo, H.S., Ooi, C.Y., Inoue, M., Ismail, N., Moghbel, M., Baskara Dass, S., Kok, C.H., Hussin, F.A. |
|---|---|
| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.24840 / |
| Published: |
IEEE Computer Society
2019
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078343218&doi=10.1109%2fATS47505.2019.00018&partnerID=40&md5=c5da625563674630028625dd473f86be http://eprints.utp.edu.my/24840/ |
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utp-eprints.248402021-08-27T08:42:30Z Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level Choo, H.S. Ooi, C.Y. Inoue, M. Ismail, N. Moghbel, M. Baskara Dass, S. Kok, C.H. Hussin, F.A. Hardware Trojan refers to a malicious modification of an integrated circuit (IC). To eliminate the complications arising from designing an IC which includes a Trojan, it is suggested to apply Trojan detection as early as at register-transfer level (RTL). In this paper, we propose a hardware Trojan detection framework which consists of both RTL and gate-level classification using machine learning approaches to detect hardware Trojan inserted at RTL. In the experiment, all Trojan benchmarks were successfully identified without false positive detection on non-Trojan benchmark. © 2019 IEEE. IEEE Computer Society 2019 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078343218&doi=10.1109%2fATS47505.2019.00018&partnerID=40&md5=c5da625563674630028625dd473f86be Choo, H.S. and Ooi, C.Y. and Inoue, M. and Ismail, N. and Moghbel, M. and Baskara Dass, S. and Kok, C.H. and Hussin, F.A. (2019) Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level. In: UNSPECIFIED. http://eprints.utp.edu.my/24840/ |
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Universiti Teknologi Petronas |
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| description |
Hardware Trojan refers to a malicious modification of an integrated circuit (IC). To eliminate the complications arising from designing an IC which includes a Trojan, it is suggested to apply Trojan detection as early as at register-transfer level (RTL). In this paper, we propose a hardware Trojan detection framework which consists of both RTL and gate-level classification using machine learning approaches to detect hardware Trojan inserted at RTL. In the experiment, all Trojan benchmarks were successfully identified without false positive detection on non-Trojan benchmark. © 2019 IEEE. |
| format |
Conference or Workshop Item |
| author |
Choo, H.S. Ooi, C.Y. Inoue, M. Ismail, N. Moghbel, M. Baskara Dass, S. Kok, C.H. Hussin, F.A. |
| spellingShingle |
Choo, H.S. Ooi, C.Y. Inoue, M. Ismail, N. Moghbel, M. Baskara Dass, S. Kok, C.H. Hussin, F.A. Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level |
| author_sort |
Choo, H.S. |
| title |
Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level |
| title_short |
Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level |
| title_full |
Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level |
| title_fullStr |
Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level |
| title_full_unstemmed |
Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level |
| title_sort |
machine-learning-based multiple abstraction-level detection of hardware trojan inserted at register-transfer level |
| publisher |
IEEE Computer Society |
| publishDate |
2019 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078343218&doi=10.1109%2fATS47505.2019.00018&partnerID=40&md5=c5da625563674630028625dd473f86be http://eprints.utp.edu.my/24840/ |
| _version_ |
1741196876843057152 |
| score |
11.62408 |