<scp>GA</scp> ‐ <scp>ANN</scp> : An Efficient Hybrid Deep Learning Scheme for Network Intrusion Detection in <scp>IoT</scp>

ABSTRACT Intrusion detection systems (IDS) are critical to the security of the dynamic internet of things (IoT) environment. The integration of Artificial Intelligence (AI) into IDS has substantially improved network security. Particularly, deep learning techniques have shown strong potential in addressing IoT security challenges. However, the effectiveness and generalizability of these methods are highly dependent on the quality, diversity, and representativeness of the input data. Inadequate data can significantly impact model training, resulting in biased predictions, reduced accuracy, and vulnerabilities in real‐world deployments. This research aims to extract meaningful, informative, and discriminative features from the data to enhance the quality of input for deep learning models, thereby improving their learning efficiency, accuracy, and overall performance. A novel genetic algorithm‐based artificial neural network (GA‐ANN) is introduced with an advanced feature extraction method that optimizes feature vectors for various configurations of the ANN. The study thoroughly evaluates feature selection techniques for their impact on IDS performance. Selecting only relevant features, rather than including all of them, significantly reduces the size of the feature set. This optimization enhances both efficiency and detection capability by lowering computational overhead while improving accuracy. On the CIC2023 dataset containing 34 attack classes, the GA‐ANN model achieved 98.46% accuracy, surpassing conventional techniques that were limited to smaller datasets with up to 11 classes. The study contributes by tackling shortcomings in data selection quality and strengthening IDS effectiveness in dynamic and rapidly evolving IoT environments.