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...
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IEEE Computer Society
2019
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my.utp.eprints.236622021-08-19T08:08:18Z 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/23662/ |
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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_facet |
Choo, H.S. Ooi, C.Y. Inoue, M. Ismail, N. Moghbel, M. Baskara Dass, S. Kok, C.H. Hussin, F.A. |
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 |
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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/23662/ |
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1738656503809703936 |
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13.244368 |