Adapted stream region for packet marking based on DDoS attack detection in vehicular ad hoc networks

  • Raenu KolandaisamyEmail author
  • Rafidah Md. NoorEmail author
  • Muhammad Reza Z’aba
  • Ismail Ahmedy
  • Indraah Kolandaisamy


Vehicular ad hoc networks (VANETs) are a group of nodes that remain dynamically and randomly situated. VANETs are considered as one of the most prominent technologies for improving the efficiency and safety of modern transportation systems. However, the VANET is also subjected to attacks that will weaken the performance of vehicular communication. To enable communication inside the VANET system, a routing protocol helps to determine directions among nodes. VANET network nodes move very quickly from one place to another, and in that time DDoS attacks will occur in the VANET network. Therefore, it is important to implement DDoS attack detection-based communication level on the entire VANET system. The source node will send data or information to the destination using intermediate nodes, whenever a DDoS attack happens in the node. In this paper, an approach is proposed for detection of a DDoS attack in a VANET network by using the adapted stream region scheme. Once the attack occurs in the VANET network, all the data will be damaged or hacked by another attacker. To minimize the DDoS attack and optimize the assigned problem, the author is using packet marking based on adapted stream region (PMBASR) techniques on the network. The PMBASR techniques are used to trace back to the source node, then the node of origin used in RSU server for data request, and at the same time the data will receive a response in the network. Nevertheless, an analytical approach will use PMBASR to detect the DDoS attack and further improve the network performance. Finally, the Ns2 simulation result proves it to be a better result-oriented approach.


VANETs DDoS attack Safety application Ns2 simulation 



This work is supported by the Faculty of Computer Science and Information Technology, University of Malaya, under Grant GPF009D-2018 and GPF005D-2018.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.


  1. 1.
    Branitskiy A (2015) Network attack detection based on combination of neural, immune and neuro-fuzzy classifiers, vol 18. IEEEGoogle Scholar
  2. 2.
    Le A, Markopoulou A (2012) Cooperative defense against pollution attacks in network coding using spacemac. IEEE J Sel Areas Commun 30(2):442–449CrossRefGoogle Scholar
  3. 3.
    Nadeem A, Howarth MP (2014) An intrusion detection & adaptive response mechanism for MANETs. Ad Hoc Netw 13:368–380CrossRefGoogle Scholar
  4. 4.
    Cepheli Ö, Büyükçorak S, Karabulut Kurt G (2016) Hybrid intrusion detection system for DDoS attacks. J Electr Comput Eng. CrossRefGoogle Scholar
  5. 5.
    Fung CJ, Zhu Q (2016) FACID: a trust-based collaborative decision framework for intrusion detection networks. Ad Hoc Netw 53:17–31CrossRefGoogle Scholar
  6. 6.
    Kolandaisamy R, Md Noor R, Ahmedy I, Ahmad I, Reza Z’aba M, Imran M, Alnuem M (2018) A multivariant stream analysis approach to detect and mitigate DDoS attacks in vehicular ad hoc networks. Wirel Commun Mob Comput. CrossRefGoogle Scholar
  7. 7.
    Qin B, Wu Q (2011) Preserving security and privacy in large-scale VANETs. Springer, BerlinCrossRefGoogle Scholar
  8. 8.
    Zhang C, Song Y, Fang Y, Zhang Y (2011) On the price of security in large-scale wireless ad hoc networks. IEEE/ACM Trans Netw 19(2):319–332CrossRefGoogle Scholar
  9. 9.
    Sinha A, Mishra SK (2013) Preventing VANET from DoS & DDoS attack. Int J Eng Trends Technol (IJETT) 4(10):4373–4376Google Scholar
  10. 10.
    de Biasi G, Vieira LF, Loureiro AA (2018) Sentinel: defense mechanism against DDoS flooding attack in software defined vehicular network. In: 2018 IEEE International Conference on Communications (ICC). IEEE, pp 1–6Google Scholar
  11. 11.
    Niyato D, Hossain E, Wang P (2011) Optimal channel access management with QoS support for cognitive vehicular networks. IEEE Trans Mob Comput 10(4):573–591CrossRefGoogle Scholar
  12. 12.
    Vasserman EY, Hopper N (2011) Vampire attacks: draining life from wireless ad hoc sensor networks. IEEE Trans Mob Comput 12(2):318–332CrossRefGoogle Scholar
  13. 13.
    Karimazad R, Faraahi A (2011) An anomaly-based method for DDoS attacks detection using RBF neural networks. In: Proceedings of the International Conference on Network and Electronics EngineeringGoogle Scholar
  14. 14.
    Xu G, Borcea C, Iftode L (2010) A policy enforcing mechanism for trusted ad hoc networks. IEEE Trans Dependable Secure Comput 8(3):321–336Google Scholar
  15. 15.
    Jeong J, Guo S, Gu Y, He T, Du DH (2011) Trajectory-based statistical forwarding for multihop infrastructure-to-vehicle data delivery. IEEE Trans Mob Comput 11(10):1523–1537CrossRefGoogle Scholar
  16. 16.
    Mershad Khaleel, Artail Hassan (2013) A framework for secure and efficient data acquisition in vehicular ad hoc networks. IEEE Trans Veh Technol 62(2):536–543CrossRefGoogle Scholar
  17. 17.
    Kumar A, Sinha M (2014) Overview on vehicular ad hoc network and its security issues. In: 2014 International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, pp 792–797Google Scholar
  18. 18.
    Iyengar NCS, Ganapathy G (2015) Trilateral trust based defense mechanism against DDoS attacks in cloud computing environment. Cybern Inf Technol 15(2):119–140Google Scholar
  19. 19.
    RoselinMary S, Maheshwari M, Thamaraiselvan M (2013) Early detection of DOS attacks in VANET using Attacked packet detection algorithm (APDA). In: 2013 International Conference on Information Communication and Embedded Systems (ICICES). IEEE, pp 237–240Google Scholar
  20. 20.
    Singh A, Sharma P (2015) A novel mechanism for detecting DoS attack in VANET using enhanced attacked packet detection algorithm (EAPDA). In: 2015 2nd International Conference on Recent Advances in Engineering and Computational Sciences (RAECS). IEEE, pp 1–5Google Scholar
  21. 21.
    Kalkan K, Gür G, Alagöz F (2016) Filtering-based defense mechanisms against DDoS attacks: a survey. IEEE Syst J 11(4):2761–2773CrossRefGoogle Scholar
  22. 22.
    Furfaro A, Malena G, Molina L, Parise A (2015) A simulation model for the analysis of DDOS amplification attacks. In: 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim). IEEE, pp 267–272Google Scholar
  23. 23.
    Benatia MA, Khoukhi L, Esseghir M, Boulahia LM (2013) A Markov chain based model for congestion control in VANETs. In: 2013 27th International Conference on Advanced Information Networking and Applications Workshops. IEEE, pp 1021–1026Google Scholar
  24. 24.
    Shrimali G, Akella A, Mutapcic A (2009) Cooperative interdomain traffic engineering using nash bargaining and decomposition. IEEE/ACM Trans Netw 18(2):341–352CrossRefGoogle Scholar
  25. 25.
    Yu H, Zhang S, Lau VK (2010) Game theoretical power control for open-loop overlaid network MIMO systems with partial cooperation. IEEE Trans Wirel Commun 10(1):135–141CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Raenu Kolandaisamy
    • 1
    • 2
    Email author
  • Rafidah Md. Noor
    • 1
    • 4
    Email author
  • Muhammad Reza Z’aba
    • 1
  • Ismail Ahmedy
    • 1
  • Indraah Kolandaisamy
    • 3
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.Faculty of Business and Information ScienceUCSI UniversityKuala LumpurMalaysia
  3. 3.School of Business ManagementUniversity Utara MalaysiaSintokMalaysia
  4. 4.Center for Mobile Cloud Computing Research (C4MCCR), Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia

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