A Genetic Algorithm-Based Double Auction Framework for Secure and Scalable Resource Allocation in Cloud-Integrated Intrusion Detection Systems

The complexity of cloud environments challenges secure resource management, especially for intrusion detection systems (IDS). Existing strategies struggle to balance efficiency, cost fairness, and threat resilience. This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm (GA) with a “double auction” method. This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework. It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders. The GA functions as an intelligent search mechanism that identifies optimal combinations of bids from users and suppliers, addressing issues arising from the intricacies of cloud systems. Analyses proved that our method surpasses previous strategies, particularly in terms of price accuracy, speed, and the capacity to manage large-scale activities, critical factors for real-time cybersecurity systems, such as IDS. Our research integrates artificial intelligence-inspired evolutionary algorithms with market-driven methods to develop intelligent resource management systems that are secure, scalable, and adaptable to evolving risks, such as process innovation.