Open Access   Article

Adaptive Neuro-fuzzy System based Attack Detection Techniques for VANETS

Sahil Nayyar1 , Anita Suman2 , Parveen Kumar3

1 Dept.ECE, Beant College of Engineering and Technology, Gurdaspur, India.
2 Dept.ECE, Beant College of Engineering and Technology, Gurdaspur, India.
3 Dept.ECE, Beant College of Engineering and Technology, Gurdaspur, India.

Correspondence should be addressed to:

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-3 , Page no. 57-64, Mar-2018


Online published on Mar 30, 2018

Copyright © Sahil Nayyar, Anita Suman, Parveen Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library


IEEE Style Citation: Sahil Nayyar, Anita Suman, Parveen Kumar, “Adaptive Neuro-fuzzy System based Attack Detection Techniques for VANETS”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.57-64, 2018.

MLA Style Citation: Sahil Nayyar, Anita Suman, Parveen Kumar "Adaptive Neuro-fuzzy System based Attack Detection Techniques for VANETS." International Journal of Computer Sciences and Engineering 6.3 (2018): 57-64.

APA Style Citation: Sahil Nayyar, Anita Suman, Parveen Kumar, (2018). Adaptive Neuro-fuzzy System based Attack Detection Techniques for VANETS. International Journal of Computer Sciences and Engineering, 6(3), 57-64.

371 201 downloads 47 downloads


VANETs are susceptible to safety threats due to cumulative dependence upon transmission, computing, and control mechanisms. Therefore, securing the end to end communication in VANETs becomes a major area of research. Many researchers have proposed several security protocols so far to improve the integrity, confidentiality, nonrepudiation, access control, etc. to provide secure VANETs to its users. Therefore, the overall goals of security protocols of VANETs are to recognize malicious nodes in the network by using suitable mechanism. In this work trustworthiness of VANETs has been improved by using some well-known adaptive Neuro-fuzzy system tools to detect the attacks in more efficient manner. Adaptive Neuro-fuzzy system tools have been used frequently to monitor the behavior of VANETs nodes and evaluate some malicious nodes based upon already developed model using historical knowledge of the same network. Since, training of the model is based upon the various features of VANETs nodes therefore, it is able to monitor the attack even in complex environment. Extensive analysis indicates that the proposed protocol outperforms others in terms of Packet Loss Ratio, Throughput, End to End Delay and Average Download Delay.

Key-Words / Index Term

VANET, Adaptive neuro-fuzzy system, Attacks, Malicious nodes


[1] Al-Kahtani, Mohammed Saeed. "Survey on security attacks in Vehicular Ad hoc Networks (VANETs)." In Signal Processing and Communication Systems (ICSPCS), 6th International Conference on IEEE , pp. 1-9, 2012.
[2] Bibhu, Vimal, Roshan Kumar, Balwant Singh Kumar, and Dhirendra Kumar Singh. "Performance analysis of black hole attack in VANET." International Journal Of Computer Network and Information Security, vol. 4, no. 11, pp. 47, 2012.
[3] C. Lai, K. Zhang, N. Cheng, H. Li and X. Shen, "SIRC: A Secure Incentive Scheme for Reliable Cooperative Downloading in Highway VANETs," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 6, pp. 1559-1574, June 2017.
[4] Chinnasamy, A., S. Prakash, and P. Selvakumari. "Enhance trust based routing techniques against sinkhole attack in AODV based VANET." International Journal of Computer Applications, vol. 65, no. 15, 2013.
[5] Doaa Al-Terri, Hadi Otrok, Hassan Barada, Mahmoud Al-Qutayri, Yousof Al Hammadi, “Cooperative based tit-for-tat strategies to retaliate against greedy behavior in VANETs”, Computer Communications, Vol.104, Pages 108-118, 2017.
[6] Guowei Wu, Jie Wang, Yongchuan Wang, Lin Yao, “Pollution Attack Resistance Dissemination in VANETs Based on Network Coding”, Procedia Computer Science, Vol. 83, Pages 131-138, 2016.
[7] Hamssa Hasrouny, Abed Ellatif Samhat, Carole Bassil, Anis Laouiti, “VANet security challenges and solutions: A survey”, Vehicular Communications, Vol. 7, Pages 7-20, 2017.
[8] Jay Rupareliya, Sunil Vithlani, Chirag Gohel, “Securing VANET by Preventing Attacker Node Using Watchdog and Bayesian Network Theory”, Procedia Computer Science, Vol. 79, Pages 649-656, 2016.
[9] Kaur, Harbir, Sanjay Batish, and Arvind Kakaria. "An approach to detect the wormhole attack in vehicular adhoc networks." International Journal of Smart Sensors and Ad Hoc Networks (IJSSAN), pp. 86-89, 2012.
[10] K. Zaidi, M. B. Milojevic, V. Rakocevic, A. Nallanathan and M. Rajarajan, "Host-Based Intrusion Detection for VANETs: A Statistical Approach to Rogue Node Detection," in IEEE Transactions on Vehicular Technology, vol. 65, no. 8, pp. 6703-6714, Aug. 2016.
[11] Kim, Yeongkwun, Injoo Kim, and Charlie Y. Shim. "A taxonomy for DOS attacks in VANET." In Communications and Information Technologies (ISCIT), 2014 14th International Symposium on, IEEE, pp. 26-27., 2014.
[12] Lo, Nai-Wei, and Hsiao-Chien Tsai. "Illusion attack on vanet applications-a message plausibility problem." In Globecom Workshops, IEEE, pp. 1-8., 2007.
[13] Lyamin, Nikita, Alexey Vinel, Magnus Jonsson, and Jonathan Loo. "Real-time detection of denial-of-service attacks in IEEE 802.11 p vehicular networks." IEEE Communications letters, vol. 18, no. 1, pp: 110-113, 2014.
[14] Muhammad Mohsin Mehdi, Imran Raza, Syed Asad Hussain, “A game theory based trust model for Vehicular Ad hoc Networks (VANETs)”, Computer Networks, Vol. 121, Pages 152-172, 2017.
[15] Qamas Gul Khan Safi, Senlin Luo, Chao Wei, Limin Pan, Qianrou Chen, “PIaaS: Cloud-oriented secure and privacy-conscious parking information as a service using VANETs”, Computer Networks, Volume 124, Pages 33-45, 2017.
[16] Quyoom, Abdul, Raja Ali, Devki Nandan Gouttam, and Harish Sharma. "A novel mechanism of detection of denial of service attack (DoS) in VANET using Malicious and Irrelevant Packet Detection Algorithm (MIPDA)." In Computing, Communication & Automation (ICCCA), 2015 International Conference on, IEEE, pp. 414-419, 2015.
[17] R. Muthumeenakshi, T.R. Reshmi, K. Murugan, “Extended 3PAKE authentication scheme for value-added services in VANETs”, Computers & Electrical Engineering, Vol. 59, Pages 27-38, 2017.
[18] Raghad Baiad, Omar Alhussein, Hadi Otrok, Sami Muhaidat, “Novel cross layer detection schemes to detect blackhole attack against QoS-OLSR protocol in VANET”, Vehicular Communications, Vol. 5, pp. 9-17, 2016.
[19] Raw, Ram Shringar, Manish Kumar, and Nanhay Singh. "Security challenges, issues and their solutions for VANET." International Journal of Network Security & Its Applications, vol. 5, no. 5, pp. 95, 2013.
[20] D.Rewadkar, D.Doye, "Adaptive-ARW: Adaptive Autoregressive Whale Optimization Algorithm for Traffic-Aware Routing in Urban VANET", International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.303-312, 2018.