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- W2059822024 abstract "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 . Key Words : VANETs, Anomaly Detection, Big Data * 정회원, 세명대학교 정보통신학부 ** 종신회원, 세명대학교 정보통신학부(교신저자)접수일자 : 2014년 9월 20일, 수정완료 : 2014년 10월 30일게재확정일자 : 2014년 12월 12일Received: 20 September, 2014 / Revised: 30 October, 2014 /Accepted: 12 December, 2014" @default.
- W2059822024 created "2016-06-24" @default.
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- W2059822024 date "2014-12-31" @default.
- W2059822024 modified "2023-09-27" @default.
- W2059822024 title "A Big Data Application for Anomaly Detection in VANETs" @default.
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- W2059822024 doi "https://doi.org/10.7236/jiibc.2014.14.6.175" @default.
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