An Intrusion Detection System in WSN Using an Optimized Self-Attention-Based Progressive Generative Adversarial Network - R. Saravana Ram
In general, a number of data types can be sensed, processed, and transmitted over wireless communication networks. Therefore, an Intrusion Detection System in a wireless sensor network using an Optimized Self-Attention-Based Progressive Generative Adversarial Network (IDS-SAPGAN-NBOA-WSN) is proposed in this paper. The input data are gathered through the WSN-DS database. The obtained data are supplied into the pre-processing phase. An Altered Phase Preserving Dynamic Range Compression (APPDRC) is employed to eliminate data redundancy and restoration of missing values in WSN data. Then, War Strategy Optimization Algorithm (WSOA) is applied to select ten features from input data. The selected features are given to Self-Attention-based Progressive Generative Adversarial Network (SAPGAN) for classifying intrusions, like Blackhole, Gray hole, Flooding, Scheduling, and Normal attacks. In general, SAPGAN does not adapt any optimization models to define optimum parameters to ensure the attack classification. That$1 (Bs why; the Namib Beetle Optimization Algorithm (NBOA) is proposed to improve the weight parameter of SAPGAN, which precisely categorizes the attacks. The proposed IDS-SAPGAN-NBOA-WSN approach is implemented and the performance metrics, such as accuracy, precision, and sensitivity are examined. The proposed technique attains 21.12%, 29.09%, 10.23%, 15.42%, and 21.05% higher accuracy when compared with existing techniques: IDS using Conditional Generative Adversarial Network in WSN (IDS-CGAN-XGBoost-WSN), Improved binary gray wolf optimizer along support vector machine for IDS in WSN (IDS-SVM-IBGWO-WSN), Intrusion detection scheme depending on a deep neural network (IDS-DNN-WSN), Effectual intrusion detection technique based upon dynamic autoencoder (IDS-LwDAN-WSN) and Enhanced grey wolf optimizer dependent particle swarm optimizer in WSN (IDS-SVM-EGWO-WSN), respectively.s