A Hybrid Approach to Intrusion Detection in Wireless Sensor Networks using Deep Learning

There is a vast array of fields making use of WSN, including healthcare, transportation, combat, surveillance, environment, and more. Their limited energy resources, communication capabilities, and processing power define them. Because of their design and placement in dangerous, unsupervised areas, they are susceptible to assaults. The network's overall security is being enhanced with the introduction of new techniques. The three mainstays of WSN security—intrusion prevention, intrusion detection, and mitigation—are usually implemented together. The Intrusion Detection System (IDS), the second line of defense in a wireless sensor network, receives the algorithms and schemes needed to detect intrusions at various levels from this study. Hierarchical sensor networks, which are the building blocks of this study's sensor network design, include clustering and a tree-like structure for communication with the base station. At each architectural level—the node, the cluster head, and the base station—a hybrid intrusion detection system is deployed. The data modification assault was detected with the use of temporal correlations of sensor signals in the node level intrusion detection system. To identify the discrepancy between the detected value and the threshold value, data anomaly is calculated, updated, and compared. Accuracy and precision are considered while deciding on a threshold value. Handling data with care. The cluster-level intrusion detection system uses the Radial Basis function to identify malicious patterns, malicious flooding assaults, and selfish misbehavior. In order to minimize the number of iterations required for RBF training, the optimization techniques of Self Organizing Map and Particle Swarm Optimization are used. The perfect RBF is taught to identify sinkhole attacks, anomalous behavior, selective forwarding, and self-serving misconduct in base station intrusion detection systems. In order to coordinate the three IDS and identify the assaults, a hybrid approach is used. Network metrics like packet delivery ratio, throughput, control overhead, normalized overhead, latency, and average energy consumption are used to scrutinize the hybrid intrusion detection system's performance. The accuracy, false alarm rate, and detection rate are measuring sticks for intrusion detection systems. This study improves the detection rate while decreasing the false alarm rate by detecting various threats at different network levels.