%0 Conference Paper
%A Choo, H.S.
%A Ooi, C.Y.
%A Inoue, M.
%A Ismail, N.
%A Moghbel, M.
%A Baskara Dass, S.
%A Kok, C.H.
%A Hussin, F.A.
%D 2019
%F scholars:11018
%I IEEE Computer Society
%K 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
%P 98
%R 10.1109/ATS47505.2019.00018
%T Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level
%U https://khub.utp.edu.my/scholars/11018/
%V 2019-D
%X 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.
%Z cited By 6; Conference of 28th IEEE Asian Test Symposium, ATS 2019 ; Conference Date: 10 December 2019 Through 13 December 2019; Conference Code:156685