• R.PARIMALA
• Dr.S.Gunasekaran

Cloud computing environments are increasingly exposed to sophisticated cyber-attacks due to their distributed architecture, large-scale data processing, and high-dimensional network traffic. These characteristics significantly challenge the effectiveness of traditional intrusion detection systems (IDS), particularly in terms of detection accuracy and false alarm rates. Feature selection plays a vital role in addressing these issues by eliminating irrelevant and redundant features while preserving discriminative information. In this study, a comprehensive analysis of three hybrid feature selection techniques—Information Gain combined with Particle Swarm Optimization (IG-PSO), Chi-Square combined with Genetic Algorithm (χ²-GA), and Correlation-based Feature Analysis combined with Whale Optimization Algorithm (CFA-WOA)—is presented for cloud intrusion detection. The proposed IDS framework follows a three-stage architecture comprising data preprocessing, optimized feature selection, and classification. Experimental evaluations are conducted using the NSL-KDD and KDD Cup 99 benchmark datasets. The performance of the analyzed methods is assessed using standard metrics including accuracy, precision, recall, F1-score, and false alarm rate. The results demonstrate that metaheuristic-driven feature selection significantly enhances classification efficiency and reduces false alarms compared to conventional approaches, thereby improving the reliability of intrusion detection in cloud computing environments.

Cloud Computing Security, Intrusion Detection System, Feature Selection, Particle Swarm Optimization, Genetic Algorithm, Whale Optimization Algorithm, NSL-KDD, KDD Cup 99, Metaheuristic Optimization

"Analysis of Metaheuristic-Based Feature Selection Techniques for Intrusion Detection in Cloud Computing Environments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.b252-b260, January-2026, Available :http://www.jetir.org/papers/JETIR2601141.pdf

"Analysis of Metaheuristic-Based Feature Selection Techniques for Intrusion Detection in Cloud Computing Environments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppb252-b260, January-2026, Available at : http://www.jetir.org/papers/JETIR2601141.pdf