A Research Survey on the Role of Machine Learning in Encryption-Decryption Techniques in Chat Applications

Abstract

In the era of rapid digital transformation, chat applications have become an essential communication tool for individuals and businesses worldwide. The security and privacy of these platforms are paramount, necessitating robust encryption mechanisms to protect sensitive data from unauthorized access and cyber threats. Traditional encryption techniques such as Advanced Encryption Standard (AES), Rivest-Shamir-Adleman (RSA), and Elliptic Curve Cryptography (ECC) offer strong security but are increasingly challenged by evolving threats, including advancements in quantum computing and sophisticated cyberattacks. Machine Learning (ML) has emerged as a promising approach to augment encryption and decryption techniques, offering advantages such as anomaly detection, predictive threat analysis, and adaptive cryptographic mechanisms. This survey explores the integration of ML in cryptographic applications for chat security, discussing key advancements, challenges, applications, and future research directions. The study highlights how ML enhances key management, identifies vulnerabilities, and improves real-time threat detection, while also addressing computational overhead, privacy concerns, and integration complexities. With the rise of quantum computing, AI-driven encryption models may play a crucial role in developing post-quantum cryptographic frameworks, ensuring secure and resilient communication in the digital landscape.