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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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
Online Access: 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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spelling 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/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
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/
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score 11.62408