Skip to main content
SpringerLink
Log in

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

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Branitskiy A (2015) Network attack detection based on combination of neural, immune and neuro-fuzzy classifiers, vol 18. IEEE

  2. Le A, Markopoulou A (2012) Cooperative defense against pollution attacks in network coding using spacemac. IEEE J Sel Areas Commun 30(2):442–449

    Article  Google Scholar 

  3. Nadeem A, Howarth MP (2014) An intrusion detection & adaptive response mechanism for MANETs. Ad Hoc Netw 13:368–380

    Article  Google Scholar 

  4. Cepheli Ö, Büyükçorak S, Karabulut Kurt G (2016) Hybrid intrusion detection system for DDoS attacks. J Electr Comput Eng. https://doi.org/10.1155/2016/1075648

    Article  Google Scholar 

  5. Fung CJ, Zhu Q (2016) FACID: a trust-based collaborative decision framework for intrusion detection networks. Ad Hoc Netw 53:17–31

    Article  Google Scholar 

  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. https://doi.org/10.1155/2018/2874509

    Article  Google Scholar 

  7. Qin B, Wu Q (2011) Preserving security and privacy in large-scale VANETs. Springer, Berlin

    Book  Google Scholar 

  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):319–332

    Article  Google Scholar 

  9. Sinha A, Mishra SK (2013) Preventing VANET from DoS & DDoS attack. Int J Eng Trends Technol (IJETT) 4(10):4373–4376

    Google Scholar 

  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 1–6

  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):573–591

    Article  Google Scholar 

  12. Vasserman EY, Hopper N (2011) Vampire attacks: draining life from wireless ad hoc sensor networks. IEEE Trans Mob Comput 12(2):318–332

    Article  Google Scholar 

  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 Engineering

  14. Xu G, Borcea C, Iftode L (2010) A policy enforcing mechanism for trusted ad hoc networks. IEEE Trans Dependable Secure Comput 8(3):321–336

    Google Scholar 

  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):1523–1537

    Article  Google Scholar 

  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):536–543

    Article  Google Scholar 

  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 792–797

  18. Iyengar NCS, Ganapathy G (2015) Trilateral trust based defense mechanism against DDoS attacks in cloud computing environment. Cybern Inf Technol 15(2):119–140

    Google Scholar 

  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 237–240

  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 1–5

  21. Kalkan K, Gür G, Alagöz F (2016) Filtering-based defense mechanisms against DDoS attacks: a survey. IEEE Syst J 11(4):2761–2773

    Article  Google Scholar 

  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 267–272

  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 1021–1026

  24. Shrimali G, Akella A, Mutapcic A (2009) Cooperative interdomain traffic engineering using nash bargaining and decomposition. IEEE/ACM Trans Netw 18(2):341–352

    Article  Google Scholar 

  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):135–141

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Raenu Kolandaisamy, Rafidah Md. Noor, Muhammad Reza Z’aba & Ismail Ahmedy

  2. Faculty of Business and Information Science, UCSI University, Jalan Menara Gading, Kuala Lumpur, Malaysia

    Raenu Kolandaisamy

  3. School of Business Management, University Utara Malaysia, Sintok, Kedah, Malaysia

    Indraah Kolandaisamy

  4. Center for Mobile Cloud Computing Research (C4MCCR), Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Rafidah Md. Noor

Authors
  1. Raenu Kolandaisamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafidah Md. Noor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Reza Z’aba
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ismail Ahmedy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Indraah Kolandaisamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Raenu Kolandaisamy or Rafidah Md. Noor.

Ethics declarations

Conflict of interest

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

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kolandaisamy, R., Noor, R.M., Z’aba, M.R. et al. Adapted stream region for packet marking based on DDoS attack detection in vehicular ad hoc networks. J Supercomput 76, 5948–5970 (2020). https://doi.org/10.1007/s11227-019-03088-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-03088-x

Keywords

Search

Navigation