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Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking

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Department of Computer Science and Engineering, Dhaka International University (DIU), Dhaka 1205, Bangladesh
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Department of Computer Science and Engineering, Military Institute of Science and Technology (MIST), Dhaka 1216, Bangladesh
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Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in 2018 IEEE 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), Dhaka, Bangladesh, 13–15 September 2018; pp. 416–421.
Symmetry 2020, 12(1), 7; https://doi.org/10.3390/sym12010007
Received: 9 November 2019 / Revised: 26 November 2019 / Accepted: 10 December 2019 / Published: 18 December 2019
Recent advancements in software-defined networking (SDN) make it possible to overcome the management challenges of traditional networks by logically centralizing the control plane and decoupling it from the forwarding plane. Through a symmetric and centralized controller, SDN can prevent security breaches, but it can also bring in new threats and vulnerabilities. The central controller can be a single point of failure. Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent. In this research, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller. The first of which is based on machine-learning algorithm where NSL-KDD dataset with feature selection ensures the accuracy of 82% with random forest classifier using the gain ratio feature selection evaluator. In the later phase, the second approach is combined with a deep neural network (DNN)-based intrusion detection system based on gated recurrent unit-long short-term memory (GRU-LSTM) where we used a suitable ANOVA F-Test and recursive feature elimination selection method to boost classifier output and achieve an accuracy of 88%. Substantial experiments with comparative analysis clearly show that, deep learning would be a better choice for intrusion detection in OpenFlow Controller. View Full-Text
Keywords: software-defined networking; random forest; gain ratio; GRU-LSTM; ANOVA F-test; OpenFlow Controller; machine learning software-defined networking; random forest; gain ratio; GRU-LSTM; ANOVA F-test; OpenFlow Controller; machine learning
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Dey, S.K.; Rahman, M.M. Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking. Symmetry 2020, 12, 7.

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