Abstract
A machine learning based Distributed Denial of Service (DDoS) attack detection system, implemented in a virtual SDN environment testbed, has been presented in this paper. This system identifies whether any incoming traffic in a network is a DDoS type or not. To implement this approach, we applied AdaBoosting with decision stump as a weak classifier to train our model on a private network dataset in SDN environment. Our model showed up to 93% detection accuracy with a low false-positive rate. We have also tested and compared our model’s accuracy with different machine learning algorithms and presented the result.
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References
Eskca EB, Abuzaghleh O, Bondugula S, Joshi P, Nakayama T, Sultana A (2015) Software defined networks security: an analysis of issues and solutions. Int J Sci Eng Res 6:1270–1275
Ashraf J, Latif S ( 2014) Handling intrusion and DDoS attacks in software defined networks using machine learning techniques. In: Software engineering conference (NSEC), pp 55–60
Abdou A, Barrera D, van Oorschot PC (2016) What lies beneath? analyzing automated SSH bruteforce attacks. In: International conference on PASSWORDS 2015: technology and practice of passwords, pp 72–91
Qazi ZA, Jin T, Lee J, Bellala G, Arndt M, Noubir G (2013) Application awareness in SDN. ACM SIGCOMM Comput Commun Rev 43:487–488
Gupta KD, Sen S (2018) A genetic algorithm approach to regenerate image from a reduce scaled image using bit data count. BRAIN. Broad Res Artif Intell Neurosci 9:34–44
Ali ST, Sivaraman V, Radford A, Jha S (2015) A survey of securing networks using software defined networking. IEEE Trans Reliab 64:1086–1097
LongTail (2018) Longtail log analysis. Accessed 24 Nov 2018. Retrieved from http://longtail.it.marist.edu/honey/
Sommer V (2014) Anamoly detection in SDN control plane. Masters thesis, Technical university of Munich, Munich, Germany
Kim H, Feamster N (2013) Improving network management with software defined networking. IEEE Commun Mag 51:114–119
Keller E, Drutskoy D, Rexford J (2013) Scalable network virtualization in software-defined networks. IEEE Internet Comput 17:20–27
Atkinson RC, Bellekens XJ, Hodo E, Hamilton A, Tachtatzis C (2017) Shallow and deep networks intrusion detection system: a taxonomy and survey, pp 1–43
Niyaz Q, Sun W, Javaid AY, Alam M (2016) A deep learning approach for network intrusion detection system. In: International conference wireless networks and mobile communications (WINCOM), pp 258–263
Syarif I, Prugel-Bennett A, Wills G (2012) Unsupervised clustering approach for network anomaly detection. In: International conference on networked digital technologies NDT 2012: networked digital technologies, pp 135–145
Bennett KP, Demiriz A (1999) Semi-supervised support vector machines. In: Neural information processing systems, pp 1–7
Chen C, Gong Y, Tian Y (2008) Semi-supervised learning methods for network intrusion detection. IEEE Int Conf Syst Man Cybern 2603–2608
Hinton G, LeCun Y, Bengio Y (2015) Deep learning review. Weekly journal of science in nature international. Nature 521:436–444
Reza MBI, Aburomman AA (2016) Survey of learning methods in intrusion detection systems. In: International conference on advances in electrical, electronic and systems engineering (ICAEES), pp 362–365
OVS-ofctl (2018) Open flow switch management commands. OpenVSwitch.org, Last Retrieved on 24 Nov 2018. http://OpenVSwitch.org/support/dist-docs/ovs-fctl.8.txt
Mininet Team (2018) Mininet overview. Last Retrieved on 24 Nov 2018. Retrieved from http://mininet.org/overview/
Phaal P, Lavine M (2018) sflow version 5. sFlow.Org, Last Retrieved on 24 Nov 2018. Retrieved from https://sflow.org/sflow_version_5:txt
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Sen, S., Gupta, K.D., Manjurul Ahsan, M. (2020). Leveraging Machine Learning Approach to Setup Software-Defined Network(SDN) Controller Rules During DDoS Attack. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_5
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DOI: https://doi.org/10.1007/978-981-13-7564-4_5
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