An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks

Abstract

In today's modern world, the usage of technology is unavoidable, and the rapid advances in the Internet and communication fields have resulted in the expansion of wireless sensor network (WSN) technology. However, WSN has been proven to be vulnerable to security breaches. The harsh and unattended deployment of these networks, combined with their constrained resources and the volume of data generated, introduces a major security concern. WSN applications are extremely critical, it is essential to build reliable solutions that involve fast and continuous mechanisms for online stream analysis, allowing the identification of attacks and intrusions. Our aim is to develop an intelligent and efficient intrusion detection system by applying an important machine learning concept known as ensemble learning in order to improve detection performance. Although ensemble models have been proven to be useful in offline learning, they have received less attention in streaming applications. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis on a specialized WSN detection system (WSN-DS) dataset in order to classify four types of attacks: Blackhole attack, Grayhole, Flooding, and Scheduling among normal network traffic. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84 % and 97.2 %, respectively. The above models are efficient and effective in dealing with concept drift while taking into account WSN resource constraints.