A Secure Load-Balancing Mechanism in Blockchain Networks to Mitigate Malicious Threats

This paper introduces a pioneering approach to improve security and efficiency in blockchain networks through a Secure Load Balancing Mechanism.Leveraging a Fruit Fly-based Load Balancing Recurrent Diffie-Hellman (FLB-RDH) algorithm, the system addresses the critical need for robust load management amidst diverse datasets such as those from stock markets, healthcare, and network traffic.By incorporating FLB-RDH, computational loads are intelligently distributed while maintaining stringent security protocols.The system undergoes rigorous testing against brute force and Distributed Denial of Service (DDoS) attacks, utilizing an optimization fitness function for proactive threat prediction and prevention.Dynamic adjustments to virtual machine deployment optimize load balancing performance, ensuring optimal resource utilization.Furthermore, continuous monitoring of data-sharing processes and efficient task queue management further fortify the system's resilience.Employing the Diffie-Hellman protocol for secure data sharing safeguards against potential malicious threats.Performance evaluations validate the system's effectiveness, demonstrating its superiority over conventional models.The system achieves accuracy of 99.96% (network traffic), 99.98% (healthcare), and 99.97% (stock market), with low computation time (6.0-7.3 seconds) and reduced CPU usage (as low as 19%).Compared to traditional models, FLB-RDH reduces latency by 30%, improves energy efficiency by 25%, and ensures a low degree of imbalance (as low as 0.11).This research underscores FLB-RDH as a promising solution for bolstering security and efficiency in blockchain networks, offering robust protection against malicious threats while optimizing computational performance.