A Novel Framework to Detect Anomalous Nodes to Secure Wireless Sensor Networks

The application driven technology wireless sensor networks (WSNs) are developed substantially in the last decades. The technology has drawn the attention for application in the scientific as well as in industrial domains. The networks use multifunctional and cheap sensor nodes. The application of the networks ranges from military to the civilian application such as battlefield monitoring, environment monitoring and patient monitoring. The network goal is to collect the data from different environmental phenomenon in an unsupervised manner from unknown and hash environment using the resource constrained sensor nodes. The construction of the sensor nodes used in the network and the distributed nature of the network infrastructure is susceptible to various types of attacks. In order to assure the functional operation of WSNs and collecting the meaningful data from the network, detecting the anomalous node and mechanisms to secure the networks are vital. In this research paper, we have used machine learning based decision tree algorithm to determine the anomalous sensor node to provide security to the WSNs. The decision tree has the capability to deal with categorical and numerical data. The simulation work was carried out in python and the result shows the accurate detection of the anomalous node. In future, the hybrid approach combining two algorithms will be employed to further performance improvement of the model.