Abstract
Software-defined networking (SDN) is a promising approach to networking that provides an abstraction layer for the physical network. This technology has the potential to decrease the networking costs and complexity within huge data centers. Although SDN offers flexibility, it has design flaws with regard to network security. To support the ongoing use of SDN, these flaws must be fixed using an integrated approach to improve overall network security. Therefore, in this paper, we propose a recurrent neural network (RNN) model based on a new regularization technique (RNN-SDR). This technique supports intrusion detection within SDNs. The purpose of regularization is to generalize the machine learning model enough for it to be performed optimally. Experiments on the KDD Cup 1999, NSL-KDD, and UNSW-NB15 datasets achieved accuracies of 99.5%, 97.39%, and 99.9%, respectively. The proposed RNN-SDR employs a minimum number of features when compared with other models. In addition, the experiments also validated that the RNN-SDR model does not significantly affect network performance in comparison with other options. Based on the analysis of the results of our experiments, we conclude that the RNN-SDR model is a promising approach for intrusion detection in SDN environments.
Highlights
Introduction e currentInternet architecture has existed for almost three decades and is becoming a progressively complicated system. e Internet lacks the capacity to accommodate continually changing requirements and the demanding nature of present day applications
Techniques proposed to detect anomalies have included Bayesian networks, support vector machines (SVMs), and arti cial neural networks (ANN), but these proposals have su ered from excessive computational cost and high false alarm rate (FAR) [6]
Traditional machine learning methods have been replaced by a new approach, called deep learning (DL), that gives better accuracy when compared with traditional machine learning
Summary
Introduction e currentInternet architecture has existed for almost three decades and is becoming a progressively complicated system. e Internet lacks the capacity to accommodate continually changing requirements and the demanding nature of present day applications. Real-time information acquisition via the OpenFlow protocol [2] is made possible due to the ow-based nature of SDNs. the SDN architecture contains numerous security challenges concerned with the control application interface, control plane, and control data interface [3]. A signi cant network security tool is an intrusion detection system (IDS). Much research has been performed in the context of detecting anomalies in an SDN environment. While these researches showed great results, they are limited in their applicability. Techniques proposed to detect anomalies have included Bayesian networks, support vector machines (SVMs), and arti cial neural networks (ANN), but these proposals have su ered from excessive computational cost and high false alarm rate (FAR) [6].
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