Enhancing DES Encryption Efficiency through a Metrics-Centric Pipeline and AI-Driven Analytical Framework

Alaa Othman Mahmood

Abstract


This paper introduces an improved Data Encryption Standard (DES) framework that incorporates a metrics-driven data processing pipeline, AI-powered analytical decision support and a quantum-inspired entropy-based key generation process to enhance both encryption efficiency and key security. It validates data, preprocesses it, encodes it, optimizes it, encrypts it, monitors it in real-time, analyzes entropy and also analyzes the execution time, encryption speed, throughput, CPU utilization, memory consumption and key randomness. Experimental results indicate that the encryption throughput was achieved on average at 5.11 MB/s, with a maximum value of 12.15 MB/s, while the time required for encryption and decryption were on average 42.67 ms and 44.21 ms, respectively. The proposed quantum-inspired key generator has an average key entropy of 0.82 (compared with 0.68 for standard random keys) and a higher uniformity of 0.88 (compared with 0.72 for standard random keys), which corresponds to an approximately 20.6% improvement. Furthermore, the quantum-inspired approach achieved a maximum entropy of 0.95 compared with 0.89 for random generation, and its strongest keys had a collision rate of no more than 0.01%. The overall average security improvement for the AI analysis was 13.30% and the overall completion rate for the full processing pipeline was 98.50%. The findings highlight practical solutions for optimizing the performance, randomness, and security evaluation of DES-based encryption by incorporating AI-driven analysis, entropy-driven key generation, and regular monitoring.

Keywords


artificial intelligence; data processing pipeline; DES Encryption; entropy analysis; multi-source entropy-driven key generation; resource utilization

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DOI: https://doi.org/10.32520/stmsi.v15i8.6796

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