Metaheuristic-Driven Optimization for Efficient Resource Allocation in Cloud Environments

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Intrusion Detection Systems (IDS) play a pivotal role in safeguarding networks against evolving cyber threats. This research focuses on enhancing the performance of IDS using deep learning models, specifically XAI, LSTM, CNN, and GRU, evaluated on the NSL-KDD dataset. The dataset addresses limitations of earlier benchmarks by eliminating redundancies and balancing classes. A robust preprocessing pipeline, including normalization, one-hot encoding, and feature selection, was employed to optimize model inputs. Performance metrics such as Precision, Recall, F1-Score, and Accuracy were used to evaluate models across five attack categories: DoS, Probe, R2L, U2R, and Normal. Results indicate that XAI consistently outperformed other models, achieving the highest accuracy (91.2%) and Precision (91.5%) post-BAT optimization. Comparative analyses of confusion matrices and protocol distributions revealed the dominance of DoS attacks and highlighted specific model challenges with R2L and U2R classes. This study demonstrates the effectiveness of optimized deep learning models in detecting complex attacks, paving the way for robust and adaptive IDS solutions.