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Intrusion Detection Scheme Using Traffic Prediction for Wireless Industrial Networks  

Wei, Min (Department of Computer Science and Engineering, Konkuk University, Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education Chongqing University of Posts and Telecommunications)
Kim, Kee-Cheon (Department of Computer Science and Engineering, Konkuk University)
Publication Information
Journal of Communications and Networks / v.14, no.3, 2012 , pp. 310-318 More about this Journal
Abstract
Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.
Keywords
Industrial wireless; intrusion detection; security; wireless networks for industrial automation-process automation (WIA-PA);
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