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

    Research output: Contribution to journalArticle

    8 Citations (Scopus)

    Abstract

    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
    VolumeE100A
    Issue number7
    DOIs
    Publication statusPublished - 2017 Jul 1

    Fingerprint

    Learning systems
    Machine Learning
    Hardware
    Support vector machines
    Neural networks
    Support Vector Machine
    Outsourcing
    Neural Networks
    Unknown
    Classifiers
    Threshold Value
    Hardware security
    Classify
    Classifier
    Benchmark
    Demonstrate

    Keywords

    • 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

    Cite this

    @article{3a1e0227639e4353966c880338c014bb,
    title = "A hardware-trojan classification method using machine learning at gate-level netlists based on Trojan features",
    abstract = "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.",
    keywords = "Gate-level netlist, Hardware Trojan, Machine learning, Neural network (NN), Support vector machine (SVM)",
    author = "Kento Hasegawa and Masao Yanagisawa and Nozomu Togawa",
    year = "2017",
    month = "7",
    day = "1",
    doi = "10.1587/transfun.E100.A.1427",
    language = "English",
    volume = "E100A",
    pages = "1427--1438",
    journal = "IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences",
    issn = "0916-8508",
    publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",
    number = "7",

    }

    TY - JOUR

    T1 - A hardware-trojan classification method using machine learning at gate-level netlists based on Trojan features

    AU - Hasegawa, Kento

    AU - Yanagisawa, Masao

    AU - Togawa, Nozomu

    PY - 2017/7/1

    Y1 - 2017/7/1

    N2 - 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.

    AB - 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.

    KW - Gate-level netlist

    KW - Hardware Trojan

    KW - Machine learning

    KW - Neural network (NN)

    KW - Support vector machine (SVM)

    UR - http://www.scopus.com/inward/record.url?scp=85021800552&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85021800552&partnerID=8YFLogxK

    U2 - 10.1587/transfun.E100.A.1427

    DO - 10.1587/transfun.E100.A.1427

    M3 - Article

    VL - E100A

    SP - 1427

    EP - 1438

    JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

    JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

    SN - 0916-8508

    IS - 7

    ER -