Hardware-trojan classification based on the structure of trigger circuits utilizing random forests

Tatsuki Kurihara, Nozomu Togawa

研究成果: Conference contribution

5 被引用数 (Scopus)

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 based 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 the random-forest classifier. The experimental results demonstrate that the average true positive rate (TPR) becomes 63.6% and the average true negative rate (TNR) becomes 100.0%. They improve the average TPR by 14.7 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
ホスト出版物のタイトルProceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665433709
DOI
出版ステータスPublished - 2021 6月 28
イベント27th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2021 - Virtual, Online
継続期間: 2021 6月 282021 6月 30

出版物シリーズ

名前Proceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021

Conference

Conference27th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2021
CityVirtual, Online
Period21/6/2821/6/30

ASJC Scopus subject areas

  • ソフトウェア
  • エネルギー工学および電力技術
  • 電子工学および電気工学
  • 安全性、リスク、信頼性、品質管理
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • ハードウェアとアーキテクチャ

フィンガープリント

「Hardware-trojan classification based on the structure of trigger circuits utilizing random forests」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル