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

Tatsuki Kurihara, Nozomu Togawa

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1049-1060
Number of pages12
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Issue number7
Publication statusPublished - 2022 Jul


  • gate-level netlist
  • hardware Trojan
  • machine learning
  • random forest
  • trigger circuit

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics


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