研究成果

国際会議

  • Deep-Learning-Based Security Evaluation on Authentication Systems Using Arbiter PUF and Its Variants
    著者
    R. Yashiro, T. Machida, M. Iwamoto, and K. Sakiyama
    会議名
    IWSEC 2016
    LNCS 9836
    ページ
    267–285
    出版社
    Springer
    発行年
    2016
    発表日
    2016
    Abstract

    Fake integrated circuit (IC) chips are in circulation on the market, which is considered a serious threat in the era of the Internet of Things (IoTs). A physically unclonable function (PUF) is expected to be a fundamental technique to separate the fake IC chips from genuine ones. Recently, the arbiter PUF (APUF) and its variants are intensively researched aiming at using for a secure authentication system. However, vulnerability of APUFs against machine-learning attacks was reported. Upon the situation, the double arbiter PUF (DAPUF), which has a tolerance against support vector machine (SVM)-based machine-learning attacks, was proposed as another variant of APUF in 2014. In this paper, we perform a security evaluation for authentication systems using APUF and its variants against Deep-learning (DL)-based attacks. DL has attracted attention as a machine-learning method that produces better results than SVM in various research fields. Based on the experimental results, we show that these DAPUFs could be used as a core primitive in a secure authentication system if setting an appropriate threshold to distinguish a legitimate IC tags from fake ones.