TY  - CONF
N1  - cited By 6; Conference of 28th IEEE Asian Test Symposium, ATS 2019 ; Conference Date: 10 December 2019 Through 13 December 2019; Conference Code:156685
AV  - none
A1  - Choo, H.S.
A1  - Ooi, C.Y.
A1  - Inoue, M.
A1  - Ismail, N.
A1  - Moghbel, M.
A1  - Baskara Dass, S.
A1  - Kok, C.H.
A1  - Hussin, F.A.
PB  - IEEE Computer Society
TI  - Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level
Y1  - 2019///
VL  - 2019-D
ID  - scholars11018
KW  - Integrated circuits; Learning systems; Machine learning; Malware
KW  -  Abstraction level; False positive detection; Gate levels; Hardware Trojan detection; Machine learning approaches; Register transfer level; Trojan detections; Trojans
KW  -  Hardware security
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078343218&doi=10.1109%2fATS47505.2019.00018&partnerID=40&md5=c5da625563674630028625dd473f86be
SN  - 10817735
N2  - 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.
ER  -