Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level

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.

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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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 2
Uncontrolled 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
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 19 Aug 2021 08:08
Last Modified: 19 Aug 2021 08:08
URI: http://scholars.utp.edu.my/id/eprint/23662

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