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    Machine Learning in Profiled Side-Channel Attacks and Low-Overhead Countermeasures

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    Date
    2019-10-18
    Author
    Raychowdhury, Arijit
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    Abstract
    Computationally secure Cryptographic algorithms, when implemented on physical hardware leak correlated physical signatures (e.g. power supply current, electromagnetic radiation, acoustic, thermal) which could be utilized to break the crypto-engine in linear time. While the existence of such side-channel attacks have been known for decades, the impact of them have been increasing with the proliferation of billions of IoT edge-devices with resource constraints. In this talk I will discuss some of our recent work on profiled attacks that take advantage of the advances in Deep Neural Networks to break AES in a few iterations. In the second half of the talk, I will describe some of the embedded hardware techniques that can provide resiliency against such power side channel attacks.
    URI
    http://hdl.handle.net/1853/62008
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    • Institute for Information Security & Privacy Cybersecurity Lecture Series [149]

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