Cryptography is essential for securing data from unauthorized access, yet Side-Channel Attacks (SCA) are becoming a more significant concern for cryptographic systems. Deep Learning-based Side-Channel Attacks (DL-SCA) exploit physical vulnerabilities like power consumption fluctuations to extract secret keys, presenting a formidable challenge compared to conventional methods.

This book focuses on evaluating DL-SCA on Advanced Encryption Standard (AES), particularly in terms of power consumption, utilizing a Convolutional Neural Network (CNN) on a microcontroller to extract keys. Two novel countermeasures are introduced: a modified AES with DL-based key scheduling using CNN and integrating SubBytes and MixColumns into a T-box operation for improved defense.

Experimental results demonstrate the effectiveness of these strategies, enhancing cryptographic security in today's interconnected digital environment. These developments create new avenues for protecting cryptographic systems from Power Analysis Attacks (PAA), contributing to ongoing efforts to safeguard sensitive data in an ever-changing digital landscape.

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