Abstract
Software-Defined Networking (SDN) introduces a new network paradigm for separating the control plane and data plane. The control plane manages the packet flow in the data plane of the network. The anomaly detection in the context of SDN is to identify potentially harmful traffic. If an anomaly occurs because of malicious packets in SDN, inspecting the payload of packets is an effective way to recognize abnormal traffic. In this paper, we consider different bandwidths and topologies of the network for the detection of an anomaly in SDN. We also evaluate the performance of the SDN on the same network. We have implemented different tree topologies on OpenFlow controller using Mininet network emulator. We considered OpenFlow messages as a performance metric for evaluating the performance of the network with different tree topologies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
(2016) Mininet: An Instant Virtual Network on yout Laptop. http://mininet.org.
(2016) POX: An Openflow controller. http://www.noxrepo.org/pox/about-po.
AppNeta. (2016). Tcpreplay. http://tcpreplay.synfin.net.
Braga, R., Mota, E., & Passito. A. (2010). Lightweight DDoS flooding attack detection using NOX/OpenFlow. In Proceedings of IEEE Conference on Local Computer Networks (LCN), pp. 408–415.
Giotis, K., Androulidakis, G., & Maglaris, V. (2014). Leveraging SDN for efficient anomaly detection and mitigation on legacy networks. In Proceedings of European Workshop on Software Defined Networks, pp. 85–90.
Giotis, K., Argyropoulos, C., Androulidakis, G., Kalogeras, D., & Maglaris, V. (2014). Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments. Computer Networks, 62, 122–136.
Gupta, H. P., Rao, S. V., & Tamarapalli, V. (2015). Analysis of stochastic \(k\)-coverage and connectivity in sensor networks with boundary deployment. IEEE Transactions on Intelligent Transportation Systems, 16(4), 1861–1871.
Gupta, H. P., Rao, S. V., & Venkatesh, T. (2016). Sleep scheduling protocol for k-coverage of three-dimensional heterogeneous wsns. IEEE Transactions on Vehicular Technology, 65(10), 8423–8431.
Gupta, H. P., Venkatesh, T., Rao, S. V., Dutta, T., & Iyer, R. R. (2017). Analysis of coverage under border effects in three-dimensional mobile sensor networks. IEEE Transactions on Mobile Computing, 16(9), 2436–2449.
Lange, S., et al. (2015). Heuristic approaches to the controller placement problem in large scale sdn networks. IEEE Transactions on Network and Service Management, 12(1), 4–17.
Lopes, F. A., Santos, M., Fidalgo, R., & Fernandes, S. (2016). A software engineering perspective on sdn programmability. IEEE Communications Surveys Tutorials, 18(2), 1255–1272.
Mehdi, S. A., Khalid, J., & Khayam, S. A. (2011). Revisiting traffic anomaly detection using software defined networking. I: Proceedings of International Symposium on Recent Advances in Intrusion Detection (RAID), Springer Berlin Heidelberg, pp. 161–180.
Prabhu, G., & Jagatheesan, S. (2018) An efficient predictive network anamoly detection and visualization. International Journal of Engineering Science, 16651.
Singh, K. J., Thongam, K., & De, T. (2018). Detection and differentiation of application layer ddos attack from flash events using fuzzy-ga computation. IET Information Security, 12(6), 502–512.
Sommer, V. (2014). Anomaly detection in the SDN control plane. Master’s thesis, Technische Universität München.
Zhang, Y. (2013). An adaptive flow counting method for anomaly detection in SDN. In Proceedings of ACM Conference on Emerging Networking Experiments and Technologies, pp. 25–30.
Acknowledgements
This work is supported by the Science and Engineering Research Board (SERB) file number ECR/2016/000406/ES, project entitled as Development of an Energy-efficient Wireless Sensor Network for Precision Agriculture, and scheme Early Career Research Award.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, A., Didwania, B., Singh, G., Gupta, H.P., Mishra, R., Dutta, T. (2020). Impact of Network Load for Anomaly Detection in Software-Defined Networking. In: Kolhe, M., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 94. Springer, Singapore. https://doi.org/10.1007/978-981-15-0694-9_13
Download citation
DOI: https://doi.org/10.1007/978-981-15-0694-9_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0693-2
Online ISBN: 978-981-15-0694-9
eBook Packages: EngineeringEngineering (R0)