Enhancing Post-Quantum Cryptography Security with BioCrypt Quantum Shield Through Nature-Inspired Machine Learning
摘要
The paper addresses the critical challenge in the field of Post-Quantum Cryptography (PQC) stemming from the advent of quantum computing, which threatens to compromise existing cryptographic systems. The paper introduces the BioCrypt Quantum Shield (BQS) as a solution, employing nature-inspired machine learning to enhance the resilience of PQC algorithms. The paper's quantitative analysis reveals notable improvements: Quantum Resistance Level (QRL) scores increased from 5,000 to 18,841.6, Operational Efficiency (OE) from 6.94 to 20.0, Scalability Scores from 6.0 to 9.5, and Adaptability Scores (AS) from 6.5 to 9.7, demonstrating the BQS model's effectiveness in addressing the urgent need for quantum-resistant cryptographic methods. Looking forward, the paper underscores the model's applicability in various sectors requiring robust security measures, suggesting a broad horizon for future research and implementation in securing digital infrastructure against the evolving landscape of quantum computing.