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Investigating Efficient Deep Learning Architectures For Side-Channel Attacks on AES
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Over the past few years, deep learning has been getting progressively more popular for the exploitation of side-channel vulnerabilities in embedded cryptographic applications, as it offers advantages in terms of the amount of attack traces required for effective key recovery. A number of effective attacks using neural networks have already been published, but reducing their cost in terms of the amount of computing resources and data required is an ever-present goal, which we pursue in this work. We focus on the ANSSI Side-Channel Attack Database (ASCAD), and produce a JAX-based framework for deep-learning-based SCA, with which we reproduce a selection of previous results and build upon them in an attempt to improve their performance. We also investigate the effectiveness of various Transformer-based models.
Forward citations
Cited by 2 Pith papers
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Machine Learning-Based AES Key Recovery via Side-Channel Analysis on the ASCAD Dataset
A comparative study shows CNNs, ResNets, and feature-selected Random Forests can recover an AES key byte from ASCAD EM traces despite near-zero classification accuracy, when evaluated with the domain-specific Key Rank metric.
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Transformers for Secure Hardware Systems: Applications, Challenges, and Outlook
A concise survey of Transformer applications in hardware security, reporting that attention-based models are increasingly used for Trojan, side-channel, and malware detection but face practical deployment hurdles.
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