Journal ArticleDOI
Survey on SDN based network intrusion detection system using machine learning approaches
TLDR
This survey evaluated the techniques of deep learning in developing SDN-based Network Intrusion Detection Systems (NIDS) and covered tools that can be used to develop NIDS models in SDN environment.Abstract:
Software Defined Networking Technology (SDN) provides a prospect to effectively detect and monitor network security problems ascribing to the emergence of the programmable features. Recently, Machine Learning (ML) approaches have been implemented in the SDN-based Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. A stream of advanced machine learning approaches – the deep learning technology (DL) commences to emerge in the SDN context. In this survey, we reviewed various recent works on machine learning (ML) methods that leverage SDN to implement NIDS. More specifically, we evaluated the techniques of deep learning in developing SDN-based NIDS. In the meantime, in this survey, we covered tools that can be used to develop NIDS models in SDN environment. This survey is concluded with a discussion of ongoing challenges in implementing NIDS using ML/DL and future works.read more
Citations
More filters
Journal ArticleDOI
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
TL;DR: A survey of deep learning approaches for cyber security intrusion detection, the datasets used, and a comparative study to evaluate the efficiency of several methods are presented.
Journal ArticleDOI
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges
TL;DR: This paper provides a comprehensive survey on the literature involving machine learning algorithms applied to SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security.
Journal ArticleDOI
A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions
TL;DR: A comprehensive detail is presented on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others.
Journal ArticleDOI
Securing the Internet of Things: Challenges, threats and solutions
TL;DR: This paper intends to provide a comprehensive security analysis of the IoT, by examining and assessing the potential threats and countermeasures, and identifies the suitable countermeasures and their limitations, paying special attention to the IoT protocols.
Journal ArticleDOI
BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset
TL;DR: The proposed end-to-end model does not use any feature engineering skills and can automatically learn the key features of the hierarchy and can well describe the network traffic behavior and improve the ability of anomaly detection effectively.
References
More filters
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings Article
An analysis of single-layer networks in unsupervised feature learning
TL;DR: In this paper, the authors show that the number of hidden nodes in the model may be more important to achieving high performance than the learning algorithm or the depth of the model, and they apply several othe-shelf feature learning algorithms (sparse auto-encoders, sparse RBMs, K-means clustering, and Gaussian mixtures) to CIFAR, NORB, and STL datasets using only single-layer networks.
Journal ArticleDOI
A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks
TL;DR: The SDN architecture and the OpenFlow standard in particular are presented, current alternatives for implementation and testing of SDN-based protocols and services are discussed, current and future SDN applications are examined, and promising research directions based on the SDN paradigm are explored.
Journal ArticleDOI
Anomaly-based network intrusion detection: Techniques, systems and challenges
TL;DR: The main challenges to be dealt with for the wide scale deployment of anomaly-based intrusion detectors, with special emphasis on assessment issues are outlined.