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http://dx.doi.org/10.7236/JIIBC.2014.14.6.175

A Big Data Application for Anomaly Detection in VANETs  

Kim, Sik (Dept. of Computer & Information Science, Semyung University)
Oh, Sun-Jin (Dept. of Computer & Information Science, Semyung University)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.14, no.6, 2014 , pp. 175-181 More about this Journal
Abstract
With rapid growth of the wireless mobile computing network technologies, various mobile ad hoc network applications converged with other related technologies are rapidly disseminated nowadays. Vehicular Ad Hoc Networks are self-organizing mobile ad hoc networks that typically have moving vehicle nodes with high speeds and maintaining its topology very short with unstable communication links. Therefore, VANETs are very vulnerable for the malicious noise of sensors and anomalies of the nodes in the network system. In this paper, we propose an anomaly detection method by using big data techniques that efficiently identify malicious behaviors or noises of sensors and anomalies of vehicle node activities in these VANETs, and the performance of the proposed scheme is evaluated by a simulation study in terms of anomaly detection rate and false alarm rate for the threshold ${\epsilon}$.
Keywords
VANETs; Anomaly Detection; Big Data;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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