Kolandaisamy, Raenu and Noor, Rafidah Md and Ahmedy, Ismail and Ahmad, Iftikhar and Reza Z'aba, Muhammad and Imran, Muhammad and Alnuem, Mohammed (2018) A Multivariant Stream Analysis Approach to Detect and Mitigate DDoS Attacks in Vehicular Ad Hoc Networks. Wireless Communications and Mobile Computing, 2018. pp. 1-13. ISSN 1530-8669, DOI https://doi.org/10.1155/2018/2874509.
Full text not available from this repository.Abstract
Vehicular Ad Hoc Networks (VANETs) are rapidly gaining attention due to the diversity of services that they can potentially offer. However, VANET communication is vulnerable to numerous security threats such as Distributed Denial of Service (DDoS) attacks. Dealing with these attacks in VANET is a challenging problem. Most of the existing DDoS detection techniques suffer from poor accuracy and high computational overhead. To cope with these problems, we present a novel Multivariant Stream Analysis (MVSA) approach. The proposed MVSA approach maintains the multiple stages for detection DDoS attack in network. The Multivariant Stream Analysis gives unique result based on the Vehicle-to-Vehicle communication through Road Side Unit. The approach observes the traffic in different situations and time frames and maintains different rules for various traffic classes in various time windows. The performance of the MVSA is evaluated using an NS2 simulator. Simulation results demonstrate the effectiveness and efficiency of the MVSA regarding detection accuracy and reducing the impact on VANET communication.
| Item Type: | Article |
|---|---|
| Funders: | Deanship of Scientific Research, King Saud University, through Research Group no. RG-1435-051 |
| Uncontrolled Keywords: | Analysis approach; Computational overheads; Detection accuracy; Distributed denial of service attack; Effectiveness and efficiencies; NS-2 simulators; Security threats; Vehicular Adhoc Networks (VANETs) |
| Subjects: | H Social Sciences > HF Commerce Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 09 Jul 2019 07:47 |
| Last Modified: | 17 Sep 2019 00:54 |
| URI: | http://eprints.um.edu.my/id/eprint/21591 |
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