COMPARATIVE ANALYSIS OF CRYPTOGRAPHIC ENCRYPTION TECHNIQUES USING AI MODELS

Abstract

Cryptography plays a fundamental role in securing digital communication, protecting sensitive information, and ensuring confidentiality, integrity, authentication, and non-repudiation in modern computing environments. With the rapid growth of internet technologies, cloud computing, Internet of Things (IoT) devices, e-commerce platforms, and digital financial systems, the need for robust encryption mechanisms has become increasingly important. Traditional cryptographic techniques such as Data Encryption Standard (DES), Advanced Encryption Standard (AES), Rivest-Shamir-Adleman (RSA), and Elliptic Curve Cryptography (ECC) have been widely adopted to secure data transmission and storage. However, the growing sophistication of cyber threats and advancements in computational technologies have created new challenges for cryptographic systems. Consequently, researchers are exploring innovative approaches that combine Artificial Intelligence (AI) and Machine Learning (ML) with cryptographic methods to enhance security performance and optimize encryption processes. This study presents a comparative analysis of cryptographic encryption techniques using AI models. The research investigates the performance, security strength, computational efficiency, and scalability of various symmetric and asymmetric encryption algorithms. Additionally, the study explores the application of machine learning models such as Decision Trees, Random Forests, Support Vector Machines, Artificial Neural Networks, and Deep Learning techniques for cryptographic evaluation and optimization. AI models are utilized to analyze encryption characteristics, predict security vulnerabilities, classify cryptographic performance levels, and support intelligent decision-making in cybersecurity environments. The findings indicate that AI-assisted analysis significantly improves the evaluation of encryption algorithms by identifying performance patterns, detecting weaknesses, and supporting adaptive security mechanisms. Symmetric algorithms such as AES demonstrate superior encryption speed, while asymmetric algorithms such as ECC provide strong security with lower key sizes compared to traditional RSA implementations. Machine learning models achieve high classification accuracy when assessing cryptographic performance and security attributes. Furthermore, AI technologies enable intelligent optimization of encryption processes and contribute to the development of adaptive cybersecurity frameworks. The study concludes that integrating Artificial Intelligence with cryptographic systems enhances both security evaluation and operational efficiency. Despite challenges related to computational complexity, dataset quality, and explainability, AI-driven cryptographic analysis represents a promising direction for future cybersecurity research. Emerging developments in post-quantum cryptography, intelligent encryption systems, and autonomous security architectures are expected to further strengthen secure communication infrastructures in the digital era.