Osprey optimization algorithm integrated with graph neural networks for intrusion detection in wireless sensor networks

With the complexity of the cyber-attacks increasing tremendously, framing an efficient intrusion detection system (IDS) has proved to be highly vital and crucial in ensuring security across the wireless sensor networks (WSNs). The major function of IDS is to prevent the WSNs from suspected attacks. Conventional IDS face numerous challenges which include limited capability, inadequate identification of attacks and detection of high false alarm rates, leading to complex data processing and pattern identification. To overcome these issues, a novel method which integrates Osprey Optimization Algorithm (OOA) and Graph Neural Networks (GNN) referred as OOA-GNN is proposed for enhancing the WSN security by efficiently detecting various categories of attacks. The proposed model integrates a deep learning framework built on graphical structures to obtain complex relationships and the hyperparameters of GNN is fine-tuned by OOA for improving the detection performance. In order to evaluate the proposed model, Wireless Sensor Networks-Dataset (WSN-DS) is chosen which is imbalanced in nature. To solve the imbalance characteristics, Synthetic Minority Oversampling Technique (SMOTE) is used on the training set. The OOA-GNN framework, through graphical representation of WSN data, successfully gathers the network patterns. The accuracy of 99.68% obtained through OOA-GNN outperforms traditional classifiers such as AdaBoost, Gradient Boosting Model (GBM), Xtreme Gradient Boosting (XGBoost), K-Nearest Neighbour-Arithmetic Optimization Algorithm (KNN-AOA), and K-Nearest Neighbour-Particle Swarm Optimization (KNN-PSO). Evaluating standard performance metrics demonstrate that the projected OOA-GNN model beats conventional approaches in terms of low false positive rate while adaption to network fluctuations. The proposed model enhances network reliability, improves the model's exactness of attack detection and decreases false alarm occurrences by integrating parameter-tuning capabilities of the OOA with graph-based neural framework in real-time WSN operations.