Opposition-Based Deer Hunting Optimization-Based Hybrid Classifier for Intrusion Detection in Wireless Sensor Networks

1. Introduction

WSNs include a small-sized self-governing wireless sensor device, which is generally placed in aggressive and vulnerable environments to monitor and collect data (Subba, et al., 2018; Deng & Liu, 2007). Despite widespread adaptation, WSNs are given to multiple restrictions associated with processing abilities, thin wireless bandwidths, random sensor node deployment, limited storage spaces, and limited battery power (Wazid & Das, 2016; Akyildiz, et al., 2002). Moreover, sensor nodes are typically less expensive and tamper-prone tools (Otoum, et al., 2019; Borkar, et al., 2019). Thus, the attacks can take control of them during physical modifications and can easily develop fake data during compromised nodes for deceiving WSN and turns out to be unproductive (Wang, et al., 2006; Salmon, et al., 2013; Qu, et al., 2018). The entire elements mentioned above can create intrusion detection and network security as a crucial part of WSNs (Qu, et al., 2018; Otoum, et al., 2018).