<![CDATA[Cloud computing environments face significant security vulnerabilities from Distributed Denial of Service (DDoS) attacks, which can cause system failures and service disruptions. Despite various existing detection methods, challenges remain regarding high computational overhead and suboptimal accuracy due to redundant features in complex datasets. This study aims to identify the optimal feature subset and evaluate its impact on detection performance across multiple machine learning algorithms for multi-class DDoS variants. The research methodology employs a two-stage approach: feature selection using Information Gain (IG) to reduce 47 original features into subsets of 8, 10, 15, and 20, followed by classification using Decision Tree (DT), Random Forest (RF), and Naïve Bayes (NB) on the CICIoT2023 dataset. Experimental results demonstrate that the Decision Tree model with an optimized subset of only 8 features, primarily Inter-Arrival Time (IAT), Header_Length, and Tot_size, achieves a superior accuracy of 99.97%. While Naïve Bayes performs well in binary classification, its accuracy drops significantly to approximately 30% in multiclass settings. This study concludes that IG-based feature selection reduces computational complexity by 30-40% while maintaining high performance across 12 DDoS variants. These findings provide a practical framework for scalable and efficient intrusion detection systems suitable for real-time deployment in resource-constrained IoT-cloud environments.]]>
