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

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.