Abstract
Traffic anomaly detection is emerging as a necessary component as wireless networks gain popularity. In this paper, based on the improved Autoregressive Integrated Moving Average (ARIMA) model, we propose a traffic anomaly detection algorithm for wireless sensor networks (WSNs) which considers the particular imbalanced, nonstationary properties of the WSN traffic and the limited energy and computing capacity of the wireless sensors at the same time. We systematically analyze the characteristics of WSN traffic, the causes of WSN abnormal traffic, and the latest related research and development. Specifically, we improve the traditional time series ARIMA model to make traffic prediction and judge the traffic anomaly in a WSN. Simulated and real WSN traffic data gathered from University of North Carolina are used to carry out simulations on Matlab. Simulation results and comparative analyses demonstrate that our proposed WSN traffic anomaly detection scheme has better anomaly detection accuracy than traditional traffic anomaly detection algorithms.
Highlights
The latest developments in distributed computing and microelectromechanical systems have enabled in the past years the emergence of various wireless sensor networks (WSNs) applications comprising military [1], home automation [2], smart building [3], healthy and medical application [4], vehicle and target tracking [5], and industry domains [6, 7]
A simulated and part of real WSN traffic data are used to carry out simulations on Matlab
The real WSN traffic data, gathered from University of North Carolina, consists of humidity measurement collected during 6-hour period at intervals of 5 seconds in 2010
Summary
The latest developments in distributed computing and microelectromechanical systems have enabled in the past years the emergence of various wireless sensor networks (WSNs) applications comprising military [1], home automation [2], smart building [3], healthy and medical application [4], vehicle and target tracking [5], and industry domains [6, 7]. A WSN consists of a large number of low cost and densely deployed battery-powered sensor nodes with wireless communication, sensing, processing, and storage capabilities [6]. Traffic anomaly detection in a WSN provides useful tools for understanding network behavior and determining network performance and reliability so as to effectively and promptly troubleshoot and resolve various issues in practice. Traffic anomaly detection in a WSN provides a sound basis for prevention and reaction in network security, as intrusions, attacks, worms, and other kinds of malicious behaviors can be identified by traffic analysis and anomaly detection
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