Attack Detection in Wireless Sensor Network: A Big Data Perspective

Abstract

For securing the network, intrusion detection systems are frequently used in wireless sensor networks to fight against insider attacks by adopting the appropriate trust-based methods. Still, the sensors could create an enormous amount of data that reduces the efficacy of trust computation in the big data era. This paper aims to introduce a new attack detection system under the big data perspective. Originally, the input data is fed the preprocessing phase, in which the normalization process takes place. Further, the MapReduce framework is used to handle the bulk data by reducing it. The preprocessed data is subjected to extract the features, where it extracts the raw features, statistical features, and higher-order statistical features. Here, the feature selection is done by chi-square ranking and info-gain ranking. Further, these features are provided as the input to the classification phase, where multilayer perception (MLP) is used for detecting the presence of an attack. For making the classification more precise, weights of MLP are optimally tuned by proposed slap swarm updated sparrow optimized algorithm which integrated the concept of sparrow search algorithm and salp swarm algorithm. Finally, the performance of presented scheme is calculated to existing approaches under different metrics.