Detection of Jamming Attack using IEWMA in Clustered Wireless Sensor Network

Wireless Sensor Networks (WSNs) in recent times, have become one of the most promising network solutions with a wide variety of applications in the areas of agriculture, environment, healthcare and the military. Notwithstanding these promising applications, sensor nodes in WSNs are vulnerable to different security attacks due to their deployment in hostile and unattended areas and their resource constraints. One of such attacks is the DoS jamming attack that interferes and disrupts the normal functions of sensor nodes in a WSN by emitting radio frequency signals to jam legitimate signals to cause a denial of service. In this work a step-wise approach using a statistical process controls technique to detect these attacks. We deploy an Improved Exponentially Weighted Moving Average (IEWMA) to detect anomalous changes in the intensity of a jamming attack event by using the packet Break Advent Time (BAT) feature of the received packets from the sensor nodes. Results obtained from a trace-driven simulation show that the proposed solution can efficiently and accurately detect jamming attacks in WSNs with little or no overhead.