University Publications

Ensuring data security is crucial in today's digital environment, where information exchange happens rapidly and continuously. Encryption plays a vital role in safeguarding sensitive data from unauthorized access and breaches. This study focuses on analyzing the encryption algorithm using hybrid elliptic curve Diffie-Hellman (ECDH) with Artificial Neural Networks (ANN), known as ECDH_ANN. Multiple input scenarios were evaluated, measuring memory complexities, operational requirements, and efficiency metrics to determine algorithm effectiveness. The primary challenge lies in improving encryption algorithms, particularly elliptic curves, and studying their complexities and performance. The ultimate goal is to measure efficiency and calculate complexities through evaluating various input scenarios, estimating execution time, memory usage, and optimizing encryption and decryption processes. This study was conducted across 50 different-sized files. The results show that as the file size grows, the encryption and decryption times also rise, while memory usage stays relatively constant, indicating efficient resource management. The algorithm maintains consistent file sizes during encryption and decryption processes, distinguishing it from algorithms that may inflate file sizes. The study also demonstrates that encryption and decryption operations exhibit linear growth rates. Overall, the ECDH_ANN algorithm stands out for its ability to maintain data integrity and use computational resources efficiently, making it perfectly suited for environments prioritizing data security and computational efficiency. The study recommends using this algorithm due to its quality and suggests comparing it with other algorithms for further analysis.