Proposed approach to detect distributed denial of service attacks in software defined network using machine learning algorithms

Abstract

SDN (Software Defined Network) is rapidly gaining importance of ‘programmable network’ infrastructure. The SDN architecture separates the Data plane (forwarding devices) and Control plane (controller of the SDN). This makes it easy to deploy new versions to the infrastructure and provides straightforward network virtualization. Distributed Denial-of-Service attack is a major cyber security threat to the SDN. It is equally vulnerable to both data plane and control plane. In this paper, machine learning algorithms such as Naïve Bayesian, KNN, K Means, K-Medoids, Linear Regression, use to classify the incoming traffic as usual or unusual. Above mentioned algorithms are measured using the two metrics: accuracy and detection rate. The best fit algorithm is applied to implement the signature IDS which forms the module 1 of the proposed IDS. Second Module uses open connections to state the exact node which is an attacker and to block that particular IP address by placing it in Access Control List (ACL), thus increasing the processing speed of SDN as a whole.