Classification of DDOS Attacks in VANETs based on Distributive Collaborative Framework
Pavan Kumar B V S P1, S.S.V.N. Sarma2, C. Lokanatha Reddy3

1Pavan Kumar B V S P, Scholar, Department of Computer Science and Engineering, Dravidian University, Kuppam, Malla Reddy Engineering College for Women, Hyderabad, Telangana.

2S.S.V.N. Sarma, Dean, Vaagdevi Engineering College, Warangal, Telangana, India.

3C. Lokanatha Reddy, Dean, School of Science & Technology, Dravidian University, Kuppam, India.

Manuscript received on 10 April 2019 | Revised Manuscript received on 17 April 2019 | Manuscript Published on 24 May 2019 | PP: 713-717 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F11410486S319/19©BEIESP

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Abstract: Distributed denial of service (DDOS) attacks is the major and consistent security and privacy problem in vehicular ad hoc vehicular networks (VANETs). Detection of denial of service attacks is a challenging task which comes under distributed and high-end vehicular networks. DDOS attacks are appeared based on different features in vehicular network classification. Traditionally mutual feature based approaches were introduced can handle relevant features relates to detection of DDOS attacks in cases of vehicular network intrusion detection. So that in this paper, we propose and present Distributed and Classification by Pattern based Framework (DCPF) for the identification of DDOS attacks in vehicular network classification. Proposed approach composed with detection of intrusion in vehicular network systems located in internet service provider (ISP) at vehicular network communications. Proposed approach also consists virtual protection rings around the vehicular network to exchange data throughout all nodes present in vehicular network classification. Proposed approach applied in real world knowledge based data set for the detection of vehicular network classification. Experimental results of proposed approach gives better and support low overhead with different vehicular network parameters in vehicular network classification.

Keywords: Vehicular Network Communication, Feature Based Selection, Internet Service Provider, Classification of Vehicular Attack sequences.
Scope of the Article: Classification