Open Access

ARTICLE

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

Siraj Un Muneer¹, Ihsan Ullah¹, Zeshan Iqbal^2,*, Rajermani Thinakaran³

1 Department of Computer Science, University of Balochistan, Quetta, 87300, Pakistan
2 Department of Computer Engineering, Sivas University of Science and Technology, Sivas, 58000, Turkey
3 Faculty of Data Science and Information Engineering, INTI International University, Nilai Campus, Nilai, 71800, Malaysia

* Corresponding Author: Zeshan Iqbal. Email:

(This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)

Computers, Materials & Continua 2025, 85(3), 4959-4975. https://doi.org/10.32604/cmc.2025.068566

Received 01 June 2025; Accepted 29 July 2025; Issue published 23 October 2025

Abstract

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.

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Keywords

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