Some of the most prominent types of attacks against modern cryptographic implementations are side-channel attacks. These attacks leverage some unintended, often physical, leakage of the implementation to retrieve secret information. In recent times, a large part of the focus of s
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Some of the most prominent types of attacks against modern cryptographic implementations are side-channel attacks. These attacks leverage some unintended, often physical, leakage of the implementation to retrieve secret information. In recent times, a large part of the focus of side-channel research has been on deep learning methods. These methods operate in a profiled setting where a model is learned based on a copy of the device that is being attacked. This model is subsequently used to create significantly more potent attacks against the target. Attacks using deep learning methods can often defeat even implementations protected with countermeasures, but as implementations become more protected, novel methods are required to successfully generate attacks.
Recently, residual neural networks have been used for side-channel attacks, and these networks show promising attacking performance. However, these novel networks are relatively limited, and a more thorough investigation into the construction of residual networks in the side-channel context is required.
Our contribution is a more thorough investigation into the construction of these residual architectures. We explore several important factors to the construction of these models and generate insights into various methods for this construction. The resulting architectures we find show attacking performance that is competitive with the state-of-the-art methods across various data sets and feature selection scenarios.
