Mitigating DDoS Attacks in Wireless Sensor Networks using Heuristic Feature Selection with Deep Learning Model

Abstract

A wireless sensor network (WSN) encompasses a massive set of sensors with limited abilities for gathering sensitive data. Since security is a significant issue in WSN, there is a possibility of different types of attacks. In Distributed Denial of Service (DDOS) attack, the malicious node can adapt to several attacks, namely flooding, black hole, warm hole, etc., to interrupt the working of the WSN. The recently developed deep learning (DL) models can effectively detect DDoS attacks in the network. Therefore, this article proposes a heuristic feature selection with a Deep Learning-based DDoS (HFSDL-DDoS) attack detection model in WSN. The proposed HFSDL-DDoS technique intends to identify and categorize the occurrence of DDoS attacks in WSN. In addition, the HFSDL-DDoS technique involves the immune clonal genetic algorithm (ICGA) based feature selection (FS) approach to improve the detection performance. Moreover, a fruit fly algorithm (FFA) with bidirectional long, short-term memory (BiLSTM) based classification model is employed. The experimental analysis of the HFSDL-DDoS technique is performed, and the results are examined interms of several performance measures. The resultant experimental results pointed out the betterment of the HFSDL-DDoS technique over the other techniques.