Generating Adversarial Examples for Hardware-Trojan Detection at Gate-Level Netlists

• Item type: Journal(1)
• 公開日: 2021-03-15
• タイトル: Generating Adversarial Examples for Hardware-Trojan Detection at Gate-Level Netlists
• タイトル / 言語: en
• タイトル: Generating Adversarial Examples for Hardware-Trojan Detection at Gate-Level Netlists
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：組込みシステム工学] hardware Trojan, netlist, logic gate, machine learning, adversarial example
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属 / 値: Dept. Computer Science and Communications Engineering, Waseda University
• 著者所属 / 値: Dept. Computer Science and Communications Engineering, Waseda University
• 著者所属 / 値: KDDI Research, Inc.
• 著者所属 / 値: KDDI Research, Inc.
• 著者所属 / 値: Research Innovation Center, Waseda University
• 著者所属 / 値: Dept. Computer Science and Communications Engineering, Waseda University
• 著者所属(英) / 言語: en
• 著者所属(英) / 値: Dept. Computer Science and Communications Engineering, Waseda University
• 著者所属(英) / 言語: en
• 著者所属(英) / 値: Dept. Computer Science and Communications Engineering, Waseda University
• 著者所属(英) / 言語: en
• 著者所属(英) / 値: KDDI Research, Inc.
• 著者所属(英) / 言語: en
• 著者所属(英) / 値: KDDI Research, Inc.
• 著者所属(英) / 言語: en
• 著者所属(英) / 値: Research Innovation Center, Waseda University
• 著者所属(英) / 言語: en
• 著者所属(英) / 値: Dept. Computer Science and Communications Engineering, Waseda University
• 著者名: Kohei, Nozawa; Kento, Hasegawa; Seira, Hidano; Shinsaku, Kiyomoto; Kazuo, Hashimoto; Nozomu, Togawa
• 著者名(英): Kohei, Nozawa; Kento, Hasegawa; Seira, Hidano; Shinsaku, Kiyomoto; Kazuo, Hashimoto; Nozomu, Togawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Recently, the great demand for integrated circuits (ICs) drives third parties to be involved in IC design and manufacturing steps. At the same time, the threat of injecting a malicious circuit, called a hardware Trojan, by third parties has been increasing. Machine learning is one of the powerful solutions for detecting hardware Trojans. However, a weakness of such a machine-learning-based classification method against adversarial examples (AEs) has been reported, which causes misclassification by adding perturbation in input samples. This paper firstly proposes a framework generating adversarial examples for hardware-Trojan detection at gate-level netlists utilizing neural networks. The proposed framework replaces hardware Trojan circuits with logically equivalent ones, and makes it difficult to detect them. Secondly, we propose a Trojan-net concealment degree (TCD) and a modification evaluating value (MEV) as measures of the amount of modifications. Finally, based on the MEV, we pick up adversarial modification patterns to apply to the circuits against hardware-Trojan detection. The experimental results using benchmarks demonstrate that the proposed framework successfully decreases the true positive rate (TPR) by a maximum of 30.15 points.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.236; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Recently, the great demand for integrated circuits (ICs) drives third parties to be involved in IC design and manufacturing steps. At the same time, the threat of injecting a malicious circuit, called a hardware Trojan, by third parties has been increasing. Machine learning is one of the powerful solutions for detecting hardware Trojans. However, a weakness of such a machine-learning-based classification method against adversarial examples (AEs) has been reported, which causes misclassification by adding perturbation in input samples. This paper firstly proposes a framework generating adversarial examples for hardware-Trojan detection at gate-level netlists utilizing neural networks. The proposed framework replaces hardware Trojan circuits with logically equivalent ones, and makes it difficult to detect them. Secondly, we propose a Trojan-net concealment degree (TCD) and a modification evaluating value (MEV) as measures of the amount of modifications. Finally, based on the MEV, we pick up adversarial modification patterns to apply to the circuits against hardware-Trojan detection. The experimental results using benchmarks demonstrate that the proposed framework successfully decreases the true positive rate (TPR) by a maximum of 30.15 points.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.236; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 62, 号 3, 発行日 2021-03-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764