Abstract
Software-defined network (SDN) consists of two layers; control and data layer that makes SDN more flexible and scalable. Open flow protocol used for SDN, which makes it simpler and easy to optimize. In this paper, we developed a SABIDS for the Python-based controller (RYU) which detects the incoming traffic by taking their flow statistics, detects the malware flow statistics (by using the pattern match technique), and identifies the malicious flow. Also, it identifies the source IP of the incoming malicious traffic and that specific IP can be blocked easily using the blacklist technique. This scheme enables the SDN controller to learn about malicious traffic and avoid the potential losses like system failure or risk of being an attack.
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Naqash, T., Tanveer, M.H., Shah, S.H., Salman, M. (2022). Statistical Analysis-Based Intrusion Detection for Software Defined Network. In: Zhang, YD., Senjyu, T., So-In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. Lecture Notes in Networks and Systems, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-4016-2_27
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DOI: https://doi.org/10.1007/978-981-16-4016-2_27
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