Hardware-Trojan Detection Based on the Structural Features of Trojan Circuits Using Random Forests

Tatsuki Kurihara, Nozomu Togawa

研究成果: Article査読

抄録

Recently, with the spread of Internet of Things (IoT) devices, embedded hardware devices have been used in a variety of everyday electrical items. Due to the increased demand for embedded hardware devices, some of the IC design and manufacturing steps have been outsourced to third-party vendors. Since malicious third-party vendors may insert malicious circuits, called hardware Trojans, into their products, developing an effective hardware-Trojan detection method is strongly required. In this paper, we propose 25 hardware-Trojan features focusing on the structure of trigger circuits for machine-learning-based hardware-Trojan detection. Combining the proposed features into 11 existing hardware-Trojan features, we totally utilize 36 hardware-Trojan features for classification. Then we classify the nets in an unknown netlist into a set of normal nets and Trojan nets based on a random-forest classifier. The experimental results demonstrate that the average true positive rate (TPR) becomes 64.2% and the average true negative rate (TNR) becomes 100.0%. They improve the average TPR by 14.8 points while keeping the average TNR compared to existing state-of-the-art methods. In particular, the proposed method successfully finds out Trojan nets in several benchmark circuits, which are not found by the existing method.

本文言語English
ページ(範囲)1049-1060
ページ数12
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E105A
7
DOI
出版ステータスPublished - 2022 7月

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学

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