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

Hridoy Jyoti Mahanta,Amit Kumar Roy,Keshab Nath,Ketan Kotecha,Vijayakumar Varadaranjan

doi:10.1109/access.2021.3139925

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.

Highlights

A MONG all the side channel attacks, power analysis attacks [Koche(1999)] have been a major threat to modular exponentiation within RSA cryptosystems [Rivest(1978)]
Messerges et al [Messerges(1999)] were the first to show the possibilities of mounting Simple Power Analysis (SPA) and Differential Power Analysis (DPA) attacks on “squaringmultiplication” based implementation of modular exponentiation
We propose an algorithm known as nearest neighbor based on entropy (E-NN) to randomize these shares prior to execution to exhibit operation hiding for resisting SPA & DPA attacks

Summary

Introduction

A MONG all the side channel attacks, power analysis attacks [Koche(1999)] have been a major threat to modular exponentiation within RSA cryptosystems [Rivest(1978)]. Messerges et al [Messerges(1999)] were the first to show the possibilities of mounting Simple Power Analysis (SPA) and Differential Power Analysis (DPA) attacks on “squaringmultiplication” based implementation of modular exponentiation. One approach to combat against the power analysis attacks is to remove the power consumption dependency on data and operations of the cryptosystems. This can be achieved by randomizing the secret data or fundamental operations of the cryptographic algorithms. This technique is popularly known as hiding. It aims to mitigate SPA and DPA by minimizing the correlation between power consumption and secret data. Mahanta et al have presented two works to resist power analysis attacks based on data randomization [Mahanta(2017)] and operation randomization [Mahanta(2017)]

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Results

Conclusion