<![CDATA[The rapid growth of internet of things (IoT) devices have improved connectivity but also exposed networks to cyber threats. This study proposes a prediction-scoring-based ensemble deep learning model with prediction-scoring-optimized feature selection (EDLM-PSOFS) for intrusion detection in IoT systems. The model integrates random forest (RF) feature extraction with ant lion optimization (ALO)-tuned convolutional neural networks (CNNs) to balance accuracy and computational efficiency. Using the KDD Cup ’99 dataset containing 4.9 million traffic records and 41 features, the framework achieved 97% accuracy, 0.99 precision, and 0.97 recall within five epochs. Comparative evaluation shows faster convergence and reduced complexity than gated recurrent units (GRU), long short-term memory (LSTM), and support vector machine (SVM) baselines, demonstrating suitability for real-time, resource-constrained IoT deployments.]]>
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