Abstract
To prevent semiconductor counterfeits, the physical unclonable functions (PUFs) have attracted attention. Since PUFs utilize the variation of semiconductor manufacturing, physical cloning of PUFs is difficult. However, the risk of machine learning attacks, which clone the function of PUFs, has been reported. In recent years, a new machine learning attack using side-channel information, such as power consumption or electromagnetic wave generated during the operation of the PUF, was reported. Therefore, to consider the security of PUFs in the future, the evaluation of the resistance of PUFs against various attacks is very important. This study proposes a new machine learning attack using power consumption waveforms for the feed-forward arbiter PUF which is one of the typical PUFs. In experiments on a field programmable gate array (FPGA), the validity of the proposed analysis method and the vulnerability of the feed-forward arbiter PUF were clarified.
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This paper is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
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Nozaki, Y., Yoshikawa, M. (2019). Power Consumption Aware Machine Learning Attack for Feed-Forward Arbiter PUF. In: Lee, R. (eds) Computer and Information Science. ICIS 2018. Studies in Computational Intelligence, vol 791. Springer, Cham. https://doi.org/10.1007/978-3-319-98693-7_4
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DOI: https://doi.org/10.1007/978-3-319-98693-7_4
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