Enhancing Wireless Sensor Network Security with Intrusion Detection Using Recurrent Neural Networks and Optimization Algorithms

Wireless Sensor Network (WSN) comprises of a larger quantity of sensor nodes to obtain and transfer data to the base station. Nevertheless, resource-controlled nodes, utilization approaches, and communication channels present many security challenges to the WSNs. Thus, it is important to detect intrusion to increase the security features of WSNs. Network Intrusion Detection (ID) methods offer various types of services to the network, and it is becoming inevitable for some communication networks. ID system using Machine Learning (ML) methods are used to identify intrusions in the network. ML techniques are utilized in the ID system; but, the performance of ML methods is not highly accurate when detecting malicious attacks. In this study, an Enhancing Wireless Sensor Network Security with Intrusion Detection using Recurrent Neural Networks and Optimization Algorithms (EWSNID-RNNOA) model is proposed. The EWSNID-RNNOA model aims to present a promising solution for ID through optimization models. Primarily, the EWSNID-RNNOA model employs min-max normalization for data preprocessing to standardize the input features. The Tuna Swarm Optimization (TSO) method is used for optimal feature selection in ID. Moreover, the Gated Recurrent Unit (GRU) classifier is employed for the classification of attack processes. Eventually, the GRU model parameters are fine-tuned by implementing the Artificial Bee Colony (ABC) approach to optimize the performance of the method. A wide-ranging experimental study is accomplished to guarantee the importance of the EWSNID-RNNOA approach under the WSN-DS dataset. The performance validation of the EWSNID-RNNOA approach illustrates a superior accuracy value of ${99.63\%}$ over the existing techniques.