<p>Intrusion Detection Systems (IDS) are crucial to secure cloud-based infrastructures from ever-evolving cyber-attacks. However, traditional IDS models struggle with issues such as redundant features, ineffective attack classification, and outdated datasets, limiting their adaptability to emerging cyber threats. This paper presents FSEGM (Feature Selection and Ensemble Generative Model), an advanced IDS framework that integrates both feature selection and ensemble generative models to improve the efficiency and accuracy of detection. This proposed framework follows a multi-stage methodology: first, a stepwise forward and backward elimination algorithm is employed for feature selection, reducing dimensionality while preserving essential information. Next, an ensemble classification approach is applied, combining several methods, including Decision Tree (J48), Random Forest, Random Tree, and Naïve Bayes with Attribute Penalization (NBPA). A generative learning technique is then used to merge probability distributions from base classifiers, improving attack recognition. The final response phase categorizes threats into misuse-based and anomaly-based activities for proactive mitigation. Experimental investigations for the proposed model were carried out with the CIC-IDS2017 dataset, a benchmark for network intrusion detection. The results demonstrate that the proposed FSEGM method significantly outperforms state-of-the-art models across multiple evaluation metrics. The Random Forest classifier achieved 98.99% accuracy, while Decision Tree (J48) reached 99.17%, both surpassing traditional approaches such as Naïve Bayes (73.01%). Additional performance measures, including precision (0.959), recall (0.938), and ROC Area (0.994), confirm the model’s reliability and robustness in identifying diverse cyber threats, particularly DoS and PortScan attacks.</p>