Fuzzy RNN based IDS for secure routing using ACO meta heuristic algorithm in WSN

Provision of security against continuous threats through the effective detection and prevention of intrusions along with efficient and secure routing is essential for modern Wireless Sensor Network (WSN) applications. The traditional secure routing protocols and the intrusion detection systems developed with single-layer network data are not able to detect many attacks accurately leading to increased packet drops in routing. In order to address these security and routing challenges, this paper proposes a new Intrusion Detection System (IDS) which has been designed by combining spatial, temporal and fuzzy rules with Recurrent Neural Network (RNN) and whale optimization to develop a cross layer IDS by considering the application layer and network layer data of WSN. Here, the nodes are clustered based on spectral clustering approach and the data are routed securely through the Cluster Head nodes using the proposed Cluster oriented Fuzzy Ant Colony Optimization (ACO) and IDS based secure routing algorithm for effective route discovery and data delivery. Here, the data are routed securely by considering the IDS feedback to enhance the security of communication. The major advantages of the proposed cluster oriented fuzzy ACO based secure routing system with IDS are increase in security, packet delivery ratio and the overall network throughput with reduction in delay and energy consumption.