A hardware-trojan classification method using machine learning at gate-level netlists based on Trojan features

Kento Hasegawa*, Masao Yanagisawa, Nozomu Togawa

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    27 Citations (Scopus)


    Due to the increase of outsourcing by IC vendors, we face a serious risk that malicious third-party vendors insert hardware Trojans very easily into their IC products. However, detecting hardware Trojans is very difficult because today's ICs are huge and complex. In this paper, we propose a hardware-Trojan classification method for gate-level netlists to identify hardware-Trojan infected nets (or Trojan nets) using a support vector machine (SVM) or a neural network (NN). At first, we extract the five hardware-Trojan features from each net in a netlist. These feature values are complicated so that we cannot give the simple and fixed threshold values to them. Hence we secondly represent them to be a five-dimensional vector and learn them by using SVM or NN. Finally, we can successfully classify all the nets in an unknown netlist into Trojan ones and normal ones based on the learned classifiers. We have applied our machine-learning-based hardware-Trojan classification method to Trust-HUB benchmarks. The results demonstrate that our method increases the true positive rate compared to the existing state-of-the-art results in most of the cases. In some cases, our method can achieve the true positive rate of 100%, which shows that all the Trojan nets in an unknown netlist are completely detected by our method.

    Original languageEnglish
    Pages (from-to)1427-1438
    Number of pages12
    JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
    Issue number7
    Publication statusPublished - 2017 Jul 1


    • Gate-level netlist
    • Hardware Trojan
    • Machine learning
    • Neural network (NN)
    • Support vector machine (SVM)

    ASJC Scopus subject areas

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


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