Anomaly Detection using Machine Learning Techniques in Wireless Sensor Networks

Abstract

Abstract The number of Wireless sensor network (WSN) deployments have been growing so exponentially over the recent years. Due to their small size and cost-effective, WSN are attracting many industries to use them in various applications. Environmental monitoring, security of buildings and precision agriculture are few example among several other fields. However, WSN faces high security threats considering most of them are deployed in unattended nature and hostile environment. In the aim of providing secure data processing in the WSN, many techniques are proposed to protect the data privacy while being transferred from the sensors to the base station. This work is focusing on attack detection which is an essential task to secure the network and the data. Anomaly detection is a key challenge in order to ensure the security and prevent malicious attacks in wireless sensor networks. Various machine learning techniques have been used by researchers these days to detect anomalies using offline learning algorithms. On the other hand online learning classifiers have not been thoroughly addressed in the literature. Our aim is to provide an intrusion detection model compatible with the characteristics of WSN. This model is built based on information gain ratio and the online Passive aggressive classifier. Firstly, the information gain ratio is used to select the relevant features of the sensor data. Secondly, the online Passive aggressive algorithm is trained to detect and classify different type of Deny of Service attacks. The experiment was conducted on a wireless sensor network-detection system (WSN-DS) dataset. The proposed model ID-GOPA results detection rate of 96% determining whether the network is in its normal mode or exposed to any type of attack. The detection accuracy is 86%, 68%, 63%, and 46% for scheduling, grayhole, flooding and blackhole attacks, respectively, in addition to 99% for normal traffic. These results shows that our model based on offline learning can be providing good anomaly detection to the WSN and replace online learning in some cases.