Total views : 4966

Defense against Prankster Attack in VANET Using Genetic Algorithm

Affiliations

  • CSE, Chandigarh Engineering College, Landran, Mohali - 140307, Punjab, India
  • CSE, Chandigarh University, National Highway 95, Chandigarh-Ludhiana Highway, Sahibzada Ajit Singh Nagar - 140413, Punjab, India

Abstract


Objectives: Detection and prevention of prankster attack in the network. One of the popular attacks in VANET is prankster attack that is affecting at high rate in the performance of the system. Method: In this paper, an algorithm based on genetic optimization has been presented for prevention as well detection of the attack. The genetic algorithm will work on the fitness function to prevent the prankster attack and the whole simulation will be done in MATLAB 2010 scenario. Findings: From the result evaluation it has been seen that proposed algorithm is better in terms of throughput, error rate, and delay and energy consumption. Improvement: Performance of the network get enhanced highly.

Keywords

Prankster Attack, Genetic Algorithm, Optimization, VANET, Security.

Full Text:

 |  (PDF views: 228)

References


  • Pouyan AA, Alimohammadi M. Sybil Attack Detection in Vehicular Networks. Computer Science and Information Technology. 2014; 2(4):197-202.
  • Soni P. A Review of Impact of Sybil Attack in VANET’s. International Journal of Advanced Research in Computer Science and Software Engineering. 2015; 5(2):1-4.
  • Rasheed R. Springer: Privacy-Aware VANET Security: Putting Data-Centric Misbehaviour and Sybil Attack Detection Schemes into Practice. 2012; 7690:296-311.
  • Ahmed A, Farag E, Abouhogail RA, Yahya A. Performance Evaluation of Black hole Attack on VANET’s Routing Protocols. International Journal of Software Engineering & Its Applications. 2014; 8(9):39-54.
  • Tyagi P, Dembla D. Investigating the security threat in vehicular ad-hoc Networks (VANETs): Towards security engineering for safer on road transportation. New Delhi: International Conference, Advances in computing, communication and Informatics (ICACCI). 2014; p. 2084-90.
  • Reza R. Ant-based Vehicle Congestion Avoidance System using Vehicular Networks. Engineering Applications of Artificial Intelligence. 2014; 36:303-19.
  • Sekhon S. Optimizing the Ad-hoc applications in Vehicular Network using Max-Min Ant system, International Journal of Engineering and Computer Science. 2014; 3(7):1-4.
  • Shringar R. Security Challenges, Issues and Their solutions for VANET. International Journal of Network Security & Its Applications (IJNSA). 2013; 5(5):95-105.
  • Sun X, Lin X, Ho PH. Secure vehicular communications based on group signature and ID-based signature scheme. Scotland, Glasgow: Proc. IEEE ICC. 2007; p. 37-41.
  • Raya M, Hubaux JP. Securing vehicular ad hoc networks. J. Comput. Security. 2007; 15(1):39-68.
  • Lu T, Zhu J. Genetic Algorithm for Energy-Efficient QoS Multicast Routing. IEEE Communications Letters. 2013; 17(1):31-35.
  • Kaue M. Movement Abnormality Evaluation Model in the Partially Centralized VANETs for Prevention Against Sybil Attack or prankster attack. I.J. Modern Education and Computer Science. 2015; 7(1):20-27.
  • Suresh KS, Vaithiyanathan V, Venugopal S. Layered Approach for Three Dimensional Collision Free Robot Path Planning using Genetic Algorithm. Indian Journal of Science and Technology. 2015 Dec; 8(35):1-6.
  • Brindha G, Rohini G, Gnanakousalya C. Genetic Algorithm based Optimization of Single Node in Reformed-Digital Micro Fluidic Biochip. Indian Journal of Science and Technology. 2015 Nov; 8(29):1-8.
  • Suresh JS, Jongkun L. A TPM-based Architecture to Secure VANET. Indian Journal of Science and Technology. 2015 July; 8(15):1-6.
  • Malik A, Pandey B. An Intelligent Authentication Based Vehicle Initiated Broadcast-Dynamic Path Data Collection Scheme in VANET. Indian Journal of Science and Technology. 2016 Apr; 9(16):1-9.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.