Empirical evaluation and optimization of hardware-Trojan classification for gate-level netlists based on multi-layer neural networks

研究成果: Article査読

4 被引用数 (Scopus)

抄録

Recently, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, malicious third-party vendors can easily insert hardware Trojans in their products and thus we have to detect them efficiently. 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. By experimentally optimizing the structure of multi-layer neural networks, we can obtain an average of 84.8% true positive rate and an average of 70.1% true negative rate while we can obtain 100% true positive rate in some of the benchmarks, which outperforms the existing methods in most of the cases.

本文言語English
ページ(範囲)2320-2326
ページ数7
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E101A
12
DOI
出版ステータスPublished - 2018 12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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