A Novel Approach to Brute Force Attack on the DLP Based Key Exchange Algorithm Using Machine Learning Techniques

Abstract

In the field of cryptography, the Diffie-Hellman (DH) key exchange algorithm has long been recognized as a fundamental mechanism for secure key distribution over unsecured communication channels. Despite its widespread use and robustness, the DH algorithm is not immune to vulnerabilities, particularly brute force attacks, where attackers attempt to guess private keys by systematically testing possible values. These attacks, though computationally intensive, pose a significant threat to the integrity of DH-based systems. This paper presents a novel approach to optimizing brute force attacks on the Diffie-Hellman key exchange algorithm through the application of machine learning techniques. Traditional brute force methods rely on sheer computational power and time, making them resource-intensive and often impractical. However, by integrating machine learning models, our approach seeks to streamline the attack process by predicting and narrowing down potential key values based on patterns observed in key exchanges. The incorporation of machine learning not only reduces the computational overhead but also significantly decreases the time required to breach the DH algorithm’s security. Our work exposes critical vulnerabilities within the DH key exchange by demonstrating how machine learning can enhance the efficiency of brute force attacks. Through a series of experiments and simulations, we analyze the performance of various machine learning models and their ability to predict private keys with increasing accuracy.