relation: https://khub.utp.edu.my/scholars/11018/
title: Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level
creator: Choo, H.S.
creator: Ooi, C.Y.
creator: Inoue, M.
creator: Ismail, N.
creator: Moghbel, M.
creator: Baskara Dass, S.
creator: Kok, C.H.
creator: Hussin, F.A.
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.
publisher: IEEE Computer Society
date: 2019
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078343218&doi=10.1109%2fATS47505.2019.00018&partnerID=40&md5=c5da625563674630028625dd473f86be
relation: 10.1109/ATS47505.2019.00018
identifier: 10.1109/ATS47505.2019.00018