eprintid: 11018 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/10/18 datestamp: 2023-11-10 03:25:35 lastmod: 2023-11-10 03:25:35 status_changed: 2023-11-10 01:14:21 type: conference_item metadata_visibility: show creators_name: Choo, H.S. creators_name: Ooi, C.Y. creators_name: Inoue, M. creators_name: Ismail, N. creators_name: Moghbel, M. creators_name: Baskara Dass, S. creators_name: Kok, C.H. creators_name: Hussin, F.A. title: Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level ispublished: pub keywords: Integrated circuits; Learning systems; Machine learning; Malware, Abstraction level; False positive detection; Gate levels; Hardware Trojan detection; Machine learning approaches; Register transfer level; Trojan detections; Trojans, Hardware security note: cited By 6; Conference of 28th IEEE Asian Test Symposium, ATS 2019 ; Conference Date: 10 December 2019 Through 13 December 2019; Conference Code:156685 abstract: 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. date: 2019 publisher: IEEE Computer Society official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078343218&doi=10.1109%2fATS47505.2019.00018&partnerID=40&md5=c5da625563674630028625dd473f86be id_number: 10.1109/ATS47505.2019.00018 full_text_status: none publication: Proceedings of the Asian Test Symposium volume: 2019-D pagerange: 98 refereed: TRUE isbn: 9781728126951 issn: 10817735 citation: 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.