Using Genetic Algorithm in Inner Product to Resist Modular Exponentiation From Higher Order DPA Attacks

Abstract

Evolutionary computation techniques have always provided fascinating results in all the fields of science and engineering. However in the area of computer security, their contribution has been comparatively very less. More specifically if we consider the side-channel attacks, use of these nature based techniques have been very nominal. Therefore, we proposed a secure protocol in this paper to combat against the higher order differential power analysis attacks on modular exponentiation based cryptosystems using one of the popular evolutionary computation techniques. The proposed work first uses Genetic Algorithm for splitting the huge exponent within the modular exponentiation into multiple non-uniform shares. Then, this shares are randomly chosen for computing individual modular exponentiation with the help of nearest neighbor algorithm. Using Genetic Algorithm, our proposed protocol can generate reasonable number of shares which exposes secret exponent at the least. As a result, it provides significant resistance to higher order differential power analysis attacks. Moreover, randomization in computing individual modular exponentiation secures the cryptosystem from generic power analysis attacks like SPA and DPA.

Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method

“…The network traffic generated by the adversary can be identified using the proposed model. To mitigate the higher-order differential power-analysis attack, a model based on evolutionary computation is proposed in [45]. First, GA is used to split the content into nonuniform shares.…”