Neural Network Based Attack on a Masked Implementation of AES

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    Masked implementations of cryptographic algorithms are often used in commercial embedded cryptographic devices to increase their resistance to side channel attacks. In this work we show how neural networks can be used to both identify the mask value, and to subsequently identify the secret key value with a single attack trace with high probability. We propose the use of a pre-processing step using principal component analysis (PCA) to significantly increase the success of the attack. We have developed a classifier that can correctly identify the mask for each trace, hence removing the security provided by that mask and reducing the attack to being equivalent to an attack against an unprotected implementation. The attack is performed on the freely available differential power analysis (DPA) contest data set to allow our work to be easily reproducible. We show that neural networks allow for a robust and efficient classification in the context of side-channel attacks.

    DOI

    Original languageEnglish
    Title of host publication2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST),
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages106-11
    Number of pages6
    ISBN (Electronic)9781467374217
    DOIs
    Publication statusPublished - 07 May 2015
    EventIEEE International Symposium on Hardware-Oriented Security and Trust (HOST) - , United Kingdom
    Duration: 05 May 201507 May 2015

    Conference

    ConferenceIEEE International Symposium on Hardware-Oriented Security and Trust (HOST)
    CountryUnited Kingdom
    Period05/05/201507/05/2015

    ID: 16989288