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

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 br...

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Bibliographic Details
Published in:Symmetry (Basel) 2020-01, Vol.12 (1), p.7
Main Authors: Dey, Samrat Kumar, Rahman, Md. Mahbubur
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Anomalies
Artificial neural networks
Classifiers
Controllers
Cybersecurity
Datasets
Decoupling
Deep learning
False alarms
Feature selection
Internet
Intrusion detection systems
Machine learning
Neural networks
Protocol
Software
Software-defined networking
Success
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Summary: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.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym12010007