A Prototype for Intrusion Detection in Wireless Sensor Networks Using Data Mining Methods

Abstract

The Wireless Sensor Networks (WSNs) are highly distributed networks of tiny, light-weight wireless nodes, placed in large numbers to monitor the environment or system. Monitoring the system includes the measurement of physical parameters such as pressure, temperature, relative humidity and passing their data to the main node (sink). WSN faces various security attacks which can affect the overall performance and security of the system. So, it is necessary to detect and prevent the attacks on WSN. Intrusion Detection is one of the major and efficient method against attacks. Intrusion Detection Systems can act as a second line of defence and it provides security primitives to prevent attacks against computer networks. This paper focuses on a hybrid approach for intrusion detection system (IDS) based on data mining techniques. The approach is clustering analysis with the aim to improve the detection rate and decrease the false alarm rate.