Improved Hybrid Approach for Side-Channel Analysis Using Efficient Convolutional Neural Network and Dimensionality Reduction

Deep learning-based side channel attacks are burgeoning due to their better efficiency and
performance, suppressing the traditional side-channel analysis. To launch the successful attack on a particular
public key cryptographic (PKC) algorithm, a large number of samples per trace might need to be acquired
to capture all the minor useful details from the leakage information, which increases the number of features
per instance. The decreased instance-feature ratio increases the computational complexity of the deep
learning-based attacks, limiting the attack efficiency. Moreover, data class imbalance can be a hindrance
in accurate model training, leading to an accuracy paradox. We propose an efficient Convolutional Neural
Network (CNN) based approach in which the dimensionality of the large leakage dataset is reduced, and
then the data is processed using the proposed CNN based model. In the proposed model, the optimal number
of convolutional blocks is used to build powerful features extractors within the cost limit. We have also
analyzed and presented the impact of using the Synthetic Minority Over-sampling Technique (SMOTE) on
the proposed model performance. We propose that a data-balancing step should be mandatory for analysis
in the side channel attack scenario. We have also provided a performance-based comparative analysis
between proposed and existing deep learning models for unprotected and protected Elliptic curve (ECC)
Montgomery Power ladder implementations. The reduced network complexity, together with an improved
attack efficiency, promote the proposed approach to be effectively used for side-channel attacks.