Skip to main content

Advertisement

Springer Nature Link
Log in

IPR: Intelligent Proactive Routing model toward DDoS attack handling in SDN

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

Abstract

Software-Defined Networking (SDN) is a contemporary and growing technology in the field of networking. It has the benefit of decoupling the infrastructure layer and the control layer thus enabling automated provisioning. Though the SDN offers numerous benefits, such as dynamic programmability, elevated bandwidth, and cost-effectiveness, it is exposed to several security issues. The most significant issue that needs to be addressed in SDN is the Distributed Denial of Service (DDoS) attack. We propose an Intelligent Proactive Routing (IPR) model to detect and mitigate the DDOS attack. The objective of our proposed model is to reduce the controller overhead, improve accuracy of detecting attacks in minimum time period, and also minimize performance degradation. The novelty of this approach is to integrate the SFlow with the Open Flow controller to lower the overhead of the control layer. And the flow rules are inserted proactively, to avoid packet loss and unavailability of service during an attack. We evaluated our proposed model with standard algorithms, and the results reveal less detection and computational time with an average of 5secs and high accuracy of 99%. The overall results show how large the network is, in which this model can perform efficiently.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Afaq M, Rehman S, Song WC (2015) Large flows detection, marking, and mitigation based on sFlow standard in SDN. J Korea Multimed Soc 18(2):189–198

    Article  Google Scholar 

  2. Ahalawat A, Dash SS, Panda A, Babu KS (2019) Entropy based DDoS detection and mitigation in openflow enabled SDN. In 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN) (pp 1–5). IEEE

  3. Alessio B, Walter de D, Alberto DB, Robert F, Jason EG (2019) Managing a communications system based on software defined networking (sdn) architecture U.S. patent application No. 16/195,149

  4. Bawany NZ, Shamsi JA, Salah K (2017) DDoS attack detection and mitigation using SDN: methods, practices, and solutions. Arab J Sci Eng 42(2):425–441

    Article  Google Scholar 

  5. Berde P, Gerola M, Hart J, Higuchi Y, Kobayashi M, Koide T, Parulkar G (2014) ONOS: towards an open, distributed SDN OS. In Proceedings of the third workshop on Hot topics in software defined networking ACM (pp. 1–6)

  6. Bholebawa IZ, Dalal UD (2018) Performance analysis of sdn/openflow controllers: Pox versus floodlight. Wirel Pers Commun 98(2):1679–1699

    Article  Google Scholar 

  7. Bhushan K, Gupta BB (2019) Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment. J Ambient Intell Humaniz Comput 10(5):1985–1997

    Article  Google Scholar 

  8. Bispo P, Corujo D, Aguiar RL (2017) A qualitative and quantitative assessment of sdn controllers. In 2017 International young engineers forum (YEF-ECE) IEEE (pp. 6–11)

  9. Dev S, Wen B, Lee YH, Winkler S (2016) Ground-based image analysis: a tutorial on machine-learning techniques and applications. IEEE Geosci Remote Sens Mag 4(2):79–93

    Article  Google Scholar 

  10. Dixit A, Hao F, Mukherjee S, Lakshman TV, Kompella R (2013) Towards an elastic distributed SDN controller. ACM SIGCOMM Comput Commun Rev 43(4):7–12

    Article  Google Scholar 

  11. Dotcenko S, Vladyko A, Letenko I (2014) A fuzzy logic-based information security management for software-defined networks. In 16th International Conference on Advanced Communication Technology (pp 167–171). IEEE

  12. Dridi L, Zhani MF (2016) SDN-guard: DoS attacks mitigation in SDN networks. In 2016 5th IEEE International Conference on Cloud Networking (Cloudnet) (pp 212–217). IEEE

  13. Elsayed MS, Le-Khac NA, Dev S, Jurcut AD (2019) Machine-learning techniques for detecting attacks in SDN. arXiv preprintarXiv:1910.00817

  14. Erin Moriarty-Siler (2014) what is opendaylight controller? AKA: Opendaylight platform URL: https://www.sdxcentral.com/networking/sdn/definitions/opendaylight-controller/

  15. Gao S, Peng Z, Xiao B, Hu A, Song Y, Ren K (2020) Detection and mitigation of DoS attacks in software defined networks. IEEE/ACM Trans Net. https://doi.org/10.1109/TNET.2020.2983976

    Article  Google Scholar 

  16. Hatagundi MD, Kumaraswamy HV (2019) A comprehensive survey on different attacks on SDN and approaches to mitigate. In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) (pp 624–627). IEEE

  17. Hu D, Hong P, Chen Y (2017) FADM: DDoS flooding attack detection and mitigation system in software-defined networking. In GLOBECOM 2017–2017 IEEE Global Communications Conference (pp. 1–7). IEEE

  18. Jin R, Wang B (2013) Malware detection for mobile devices using software-defined networking. In 2013 Second GENI research and educational experiment workshop (pp. 81–88). IEEE

  19. Karmakar KK, Varadharajan V, Tupakula U (2019) Mitigating attacks in software defined networks. Clust Comput 22(4):1143–1157

    Article  Google Scholar 

  20. Koulouzis S, Belloum AS, Bubak MT, Zhao Z, Živković M, de Laat CT (2016) SDN-aware federation of distributed data. Futur Gener Comput Syst 56:64–76

    Article  Google Scholar 

  21. Kreutz D, Ramos F, Verissimo P, Rothenberg CE, Azodolmolky S, Uhlig S (2014) Software-defined networking: a comprehensive survey. arXiv preprint arXiv:1406.0440

  22. Krishnan P, Duttagupta S, Achuthan K (2019) VARMAN: multi-plane security framework for software defined networks. Comput Commun 148:215–239

    Article  Google Scholar 

  23. Kuerban M, Tian Y, Yang Q, Jia Y, Huebert B, Poss D (2016) Flowsec: DOS attack mitigation strategy on SDN controller. In 2016 IEEE International Conference on Networking, Architecture and Storage (NAS) (pp 1–2)

  24. Lee S, Kim T, Kim T (2017) Performance analysis and optimization of opendaylight controller in distributed cluster environment. KIPS Trans Comput Commun Syst 6(11):453–462

    Google Scholar 

  25. Li C, Wu Y, Yuan X, Sun Z, Wang W, Li X, Gong L (2018) Detection and defense of DDoS attack–based on deep learning in OpenFlow-based SDN. Int J Commun Syst 31(5):e3497

    Article  Google Scholar 

  26. Linux Foundation (2016) what is Open vSwitch? URL: http://docs.openvswitch.org/en/ latest/intro/what-is-ovs/

  27. Liu B, Yu P, Chen F, Chen F, Xue-song Q, Shi L (2019) Risk-aware service routes planning for system protection communication network in energy internet. In 2019 IFIP/IEEE Symposium on integrated network and service management (IM) (pp. 295–303). IEEE

  28. Mehdi SA, Khalid J, Khayam SA (2011) Revisiting traffic anomaly detection using software defined networking. In International workshop on recent advances in intrusion detection (pp. 161–180). Springer, Berlin, Heidelberg

  29. Mehr SY, Ramamurthy B (2019) An SVM based DDoS attack detection method for Ryu SDN controller. In Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies (pp 72–73). ACM

  30. Mininet team (2018) Emulator: mininet overview in url : http://mininet.org/overview/

  31. Nwosu CS, Dev S, Bhardwaj P, Veeravalli B, John D (2019) Predicting stroke from electronic health records. arXiv preprint arXiv:1904.11280

  32. Parewa labs python programming: https://www.programiz.com/python-programming

  33. Rana DS, Dhondiyal SA, Chamoli SK (2019) Software defined networking (SDN) challenges, issues and solution. Int J Comput Sci Eng 7(1):884–889

    Google Scholar 

  34. Rojas E, Doriguzzi-Corin R, Tamurejo S, Beato A, Schwabe A, Phemius K, Guerrero C (2018) Are we ready to drive software-defined networks? A comprehensive survey on management tools and techniques. ACM Comput Surv (CSUR) 51(2):27

    Article  Google Scholar 

  35. Sarang Narkhede (2018) Understanding AUC—ROC curve towards data science url: https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5

  36. SDx central staff (2013) what is openflow? definition and how it relates to SDN? url: https://www.sdxcentral.com/networking/sdn/definitions/what-is-openflow/

  37. Shin SW, Porras P, Yegneswara V, Fong M, Gu G, Tyson M (2013) Fresco: modular composable security services for software-defined networks. In 20th annual network and distributed system security symposium. Ndss

  38. Singh PK, Jha SK, Nandi SK, Nandi S (2018) ML-based approach to detect DDoS attack in V2I communication under SDN architecture. In TENCON 2018–2018 IEEE Region 10 Conference (pp 0144–0149). IEEE.

  39. Stefano A, Antonio P (2013) D-ITG distributed internet traffic generator documentation is in the following URL: http://www.grid.unina.it/software/ITG/ documentation.php

  40. Szwaczyk S, Wrona K, Amanowicz M (2018) Applicability of risk analysis methods to risk-aware routing in software-defined networks. In 2018 International Conference on Military Communications and Information Systems (ICMCIS) (pp. 1–7). IEEE

  41. Ujjan RMA, Pervez Z, Dahal K, Bashir AK, Mumtaz R, González J (2019) Towards sflow and adaptive polling sampling for deep learning based DDoS detection in SDN. Fut Gen Comput Syst

  42. Wang H, Xu L, Gu G (2015) Floodguard: a dos attack prevention extension in software-defined networks. In 2015 45th annual IEEE/IFIP International Conference on Dependable Systems and Networks (pp 239–250). IEEE

  43. Wang R, Jia Z, Ju L (2015) An entropy-based distributed DDoS detection mechanism in software-defined networking. In 2015 IEEE trustcom/bigdataSE/ISPA (Vol. 1, pp. 310–317). IEEE

  44. Zhang H, Wang Y, Chen H, Zhao Y, Zhang J (2017) Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks. Opt Fiber Technol 39:37–42

    Article  Google Scholar 

  45. Zhou D, Yan Z, Fu Y, Yao Z (2018) A survey on network data collection. J Netw Comput Appl 116:9–23

    Article  Google Scholar 

  46. Zhou D, Yan Z, Liu G, Atiquzzaman M (2019) An adaptive network data collection system in SDN. IEEE Trans Cogn Commun Netw. https://doi.org/10.1109/TCCN.2019.2956141

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engg, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

    R. Pradeepa & M. Pushpalatha

Authors
  1. R. Pradeepa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Pushpalatha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Pradeepa.

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

Pradeepa, R., Pushpalatha, M. IPR: Intelligent Proactive Routing model toward DDoS attack handling in SDN. J Supercomput 77, 12355–12381 (2021). https://doi.org/10.1007/s11227-021-03750-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03750-3

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy