Hardware implementations of encryption schemes are unprotected against side-channel analysis techniques. Physical realizations of secure algorithms leak side-channel information through power, noise, time, sound and electromagnetic radiation. Data-dependent correlations with this leakage are exploited to obtain secret information. Power analysis techniques are powerful, undetectable and non-intrusive attacks that allow an adversary to extracts the secret key of the encryption scheme. These techniques rely on analyzing the power consumed by these physical realizations using leakage models and statistical techniques.

Implementing a countermeasure against power analysis attacks require a thorough understanding of the attack, encryption algorithm and it's implementation on hardware and software. Conventional countermeasures for AES against power analysis techniques minimize the side-channel information by implementing masking and hiding strategies at different abstraction levels. This thesis investigates a new class of countermeasures known as "breaking" through the implementation of the Substitution Box transformation using a neural network (S-Net). The inherent properties associated with the neural network architecture is expected to remove the correlation between the power consumed and the secret key used for encryption by breaking the linear power characteristics assumed by the leakage model.

The proposed approach was implemented in software and an attack framework is used to run side-channel attacks and quantify information leakage. The effectiveness of the implemented countermeasure is measured by checking and quantifying it's security against Differential and Correlation Power Analysis, Template and Deep Learning based techniques. The results indicate that the implementation is secure against these attacks.