Time Series Analysis for Jamming Attack Detection in Wireless Networks

Abstract

Due to the open nature of wireless communication medium, wireless networks are susceptible to jamming attacks. Jammers interfere with the legitimate nodes by sending strong jamming signals. Legitimate nodes can successfully transmit only between the gaps of the jamming signals. It is therefore very important to detect a jamming attack as soon as it happens in order to effectively take counter measurements. There are various types of jamming attacks, however, the {\it signature} of all jamming attacks is the performance degradation of legitimate nodes. Based on this observation, we develop a detection method using time series analysis approach. We model the network measurements taken over time as time series, and employ a sequential change point detection algorithm to detect the change of state in the time series, which is an indicator of change in the network state. Timely and accurate detection is the first step before further identification and localization of the source of interference. In this paper, we address the detection part and leave the localization of the jammer to future work. The jamming attacks are simulated in ns-3 simulator, and the detection result is satisfactory in terms of false alarm rate and detection delay.