Hyper-parameter optimization for machine-learning based electromagnetic side-channel analysis

Side-channel attacks are the class of attacks which exploits the physical leakages of the system to recover the secret key, based on the weakness induced due to implementation of algorithm on embedded systems. AES is mathematically secure but side-channel information can lead to key recovery. Over the last decade, machine learning has been introduced in parallel along with traditional statistical side-channel analysis methods. Accurate classification u sing t he machine-learning-based approaches critically depends on various factors including, precision of the input data-sets which consist of the features, tuning of different parameters for that particular algorithm, per feature sample length, number of validation folds and feature extraction/selection methods. For analysis of leaked signals in this study, hyper-parameter tuning is carried out on the feature data-sets formed on basis of the time and frequency domain properties of the signals. Results provide the comparative analysis of the best choices and leads to concrete selection of the parameters.