Skip to main content

Security of Networks Using Efficient Adaptive Flow Counting for Anomaly Detection in SDN

  • Conference paper
  • First Online:
Artificial Intelligence and Evolutionary Computations in Engineering Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 394))

Abstract

Security of network is fundamental requirement due to the rapid growth of utilization of network. SDN is nowadays the most preferred evolving networking technology. It provides higher innovation and more integration of services. Including the rapid innovation, also there lies a threat of intrusion in separate planes. Owing to open interfaces present between different planes, risks of intrusion or anonymous traffic inside the network increases. Therefore, on high-traffic networks, monitoring and measurement of traffic is a main area of concern. Several anomaly detection techniques had already been provided for this cause. But still there is a need of efficient anomaly detection methods so that network can work smoothly and intrusion-free with the proper utilization of networking resources. This paper describes a work towards enhancing the efficiency of anomaly detection method while preserving the performance of our network. Also network overhead, response time, and controller workload must be considered while applying monitoring policies. Focus will be on implementing an efficient adaptive flow counting mechanism so that anomaly can be detected dynamically, but the aggregation rules must be modified accordingly.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Betts M, Fratini S, Davis N, Dolin R. SDN architecture. In: Open networking foundation ONF SDN ARCH 1.0 06062014. Issue 1 (2014).

    Google Scholar 

  2. Akhunzada A, Ahmed E, Gani A, Khan MK, Imran M, Guizani S. Securing the software defined networks: taxonomy, requirements, and open issues. In: IEEE communication magazine. 2014.

    Google Scholar 

  3. Garg G, Garg R. Review on architecture and security issues in SDN. Int J Innov Res Comput Commun Eng. 2014;2(11):6519–24.

    Google Scholar 

  4. Bozakov Z, Papadimitriou P. Towards a scalable software-defined network virtualization platform. In: IEEE network operations and management symposium. 2014. p. 1–8.

    Google Scholar 

  5. Zseby T, Hirch T, Claise B. Packet sampling for flow accounting: challenges and limitations. In: Passive and active network measurement. Lecture notes in computer science, vol. 4979. Springer. 2008. p. 61–71.

    Google Scholar 

  6. Mai J, Sridharan A, Chuah CN, Zang H, Ye T. Impact of Packet Sampling on Portscan Detection. IEEE J Selected Areas Commun. 2006;24(12):2285–98.

    Article  Google Scholar 

  7. Zhang Y. An adaptive flow counting method for anomaly detection in SDN. ACM Digital library. In: Proceedings of CoNEXT, Santa Barbara, California, USA. 2013. p. 25–30.

    Google Scholar 

  8. Banford P, Kline J, Plonka D, Ron A. A signal analysis of network traffic anomalies. ACM Digital library. In: Proceedings of SIGCOMM IMW’02. 2002. p. 71–82.

    Google Scholar 

  9. Lakhina A, Crovella M, Diot C. Mining anomalies using traffic feature distributions. ACM Digital library. In: Proceedings of SIGCOMM, Philadelphia Pennsylvania, USA. 2005. p. 217–228.

    Google Scholar 

  10. Giotis K, Androulidakis G, Maglaris V. Leveraging SDN for efficient anomaly detection and mitigation on legacy networks. In: Proceedings of third European workshop on software defined networks (EWSDN), Budapest, Hungary. 2013.

    Google Scholar 

  11. Mehdi SA, Khalid J, Khayam SA. Revisiting traffic anomaly detection using software defined networking. In: Recent advances in intrusion detection. Springer. 2011.

    Google Scholar 

  12. Moshref M, Yu M, Govindan R. Resource/accuracy tradeoffs in software-defined measurement. ACM Digital Library. In: Proceedings of HotSDN’13, Hong Kong, China. 2013. p. 73–78.

    Google Scholar 

  13. Garg G, Garg R. Detecting anomalies efficiently in SDN using adaptive mechanism. In: IEEE, International conference on advance computing and communication technologies (ACCT2015) Rohtak, INDIA. 2015.

    Google Scholar 

Download references

Acknowledgments

I would like to give my sincere gratitude to all the friends and colleagues who were helping me to conduct this research, without whom this research would be incomplete.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gagandeep Garg .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this paper

Cite this paper

Garg, G., Garg, R. (2016). Security of Networks Using Efficient Adaptive Flow Counting for Anomaly Detection in SDN. In: Dash, S., Bhaskar, M., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 394. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2656-7_61

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2656-7_61

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2654-3

  • Online ISBN: 978-81-322-2656-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics