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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: Proceedings of the Asian Test Symposium.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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 (Paper)
Impact Factor:cited By 2
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Academic Subject One:Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Design
ID Code:23024
Deposited By: Mr Ahmad Suhairi
Deposited On:18 Aug 2021 12:58
Last Modified:18 Aug 2021 12:58

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