Hardware Trojans classification for gate-level netlists using multi-layer neural networks

Kento Hasegawa; Masao Yanagisawa; Nozomu Togawa

doi:10.1109/IOLTS.2017.8046227

Hardware Trojans classification for gate-level netlists using multi-layer neural networks

Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa

School of Fundamental Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

41 Citations (Scopus)

Abstract

Recently, due to the increase of outsourcing in IC design and manufacturing, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, it is strongly required to detect hardware Trojans because malicious third-party vendors can easily insert hardware Trojans in their products. In this paper, we propose a machine-learning-based hardware-Trojan detection method for gate-level netlists using multi-layer neural networks. First, we extract 11 Trojan-net feature values for each net in a netlist. After that, we classify the nets in an unknown netlist into a set of Trojan nets and that of normal nets using multi-layer neural networks. We obtained at most 100% true positive rate with our proposed method.

Original language	English
Title of host publication	2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	227-232
Number of pages	6
ISBN (Electronic)	9781538603512
DOIs	https://doi.org/10.1109/IOLTS.2017.8046227
Publication status	Published - 2017 Sep 19
Event	23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 - Thessaloniki, Greece Duration: 2017 Jul 3 → 2017 Jul 5

Publication series

Name	2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017

Other

Other	23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017
Country	Greece
City	Thessaloniki
Period	17/7/3 → 17/7/5

Keywords

Gate-level netlist
Hardware Trojan
Machine learning
Multi-layer
Neural network

ASJC Scopus subject areas

Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Computer Networks and Communications
Hardware and Architecture
Control and Systems Engineering

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Hasegawa, K., Yanagisawa, M., & Togawa, N. (2017). Hardware Trojans classification for gate-level netlists using multi-layer neural networks. In 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 (pp. 227-232). [8046227] (2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IOLTS.2017.8046227

Hardware Trojans classification for gate-level netlists using multi-layer neural networks. / Hasegawa, Kento; Yanagisawa, Masao ; Togawa, Nozomu.

2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 227-232 8046227 (2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hasegawa, K, Yanagisawa, M & Togawa, N 2017, Hardware Trojans classification for gate-level netlists using multi-layer neural networks. in 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017., 8046227, 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017, Institute of Electrical and Electronics Engineers Inc., pp. 227-232, 23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017, Thessaloniki, Greece, 17/7/3. https://doi.org/10.1109/IOLTS.2017.8046227

Hasegawa K, Yanagisawa M , Togawa N. Hardware Trojans classification for gate-level netlists using multi-layer neural networks. In 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 227-232. 8046227. (2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017). https://doi.org/10.1109/IOLTS.2017.8046227

Hasegawa, Kento ; Yanagisawa, Masao ; Togawa, Nozomu. / Hardware Trojans classification for gate-level netlists using multi-layer neural networks. 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 227-232 (2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017).

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abstract = "Recently, due to the increase of outsourcing in IC design and manufacturing, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, it is strongly required to detect hardware Trojans because malicious third-party vendors can easily insert hardware Trojans in their products. In this paper, we propose a machine-learning-based hardware-Trojan detection method for gate-level netlists using multi-layer neural networks. First, we extract 11 Trojan-net feature values for each net in a netlist. After that, we classify the nets in an unknown netlist into a set of Trojan nets and that of normal nets using multi-layer neural networks. We obtained at most 100% true positive rate with our proposed method.",

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Hardware Trojans classification for gate-level netlists using multi-layer neural networks

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Keywords

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