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Optimal solution for malicious node detection and prevention using hybrid chaotic particle dragonfly swarm algorithm in VANETs - 2020

Research Area:  Vehicular Ad Hoc Networks

Abstract:

Vehicular ad hoc networks (VANETs) have the ability to make changes in travelling and driving mode of people and so on, in which vehicle can broadcast and forward the message related to emergency or present road condition. The safety and efficiency of modern transportation system is highly improved using VANETs. However, the vehicular communication performance is weakened with the sudden emergence of distributed denial of service (DDoS) attacks. Among other attacks, DDoS attack is the fastest attack degrading the VANETs performance due to its node mobility nature. Also, the attackers (cyber terrorists, politicians, etc.) have now considered the DDoS attack as a network service degradation weapon. In current trend, there is a quick need for mitigation and prevention of DDoS attacks in the exploration field. To resolve the conflict of privacy preservation, we propose a fast and secure HCPDS based framework for DDoS attack detection and prevention in VANETs. The Road Side Units (RSUs) have used HCPDS algorithm to evaluate the fitness values of all vehicles. This evaluation process is done for effective detection of spoofing and misbehaving nodes by comparing the obtained fitness value with the statistical information (packet factors, RSU zone, and vehicle dynamics) gathered from the vehicles. The credentials of all worst nodes are cancelled to avoid further communication with other vehicles. In HCPDS algorithm, the PSO updation strategy is added to Dragon fly algorithm to improve the search space. In addition, Chaos theory is applied to tune the parameters of proposed HCPDS algorithm. From the experimental results, it proved that the HCPDS based proposed approach can efficiently meet the requirements of security and privacy in VANETs.

Author(s) Name:  S. Prabakeran & T. Sethukarasi

Journal name:  Wireless Networks

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s11276-020-02413-0

Volume Information:  volume 26, pages 5897–5917 (2020)