@inproceedings{1f614b714827464cbfdacb32df0ab4eb,
title = "Intelligent intrusion detection using radial basis function neural network",
abstract = "Recently we witness a booming and ubiquity evolving of internet connectivity all over the world leading to dramatic amount of network activities and large amount of data and information transfer. Massive data transfer composes a fertile ground to hackers and intruders to launch cyber-attacks and various types of penetrations. As a consequence, researchers around the globe have devoted a large room for researches that can handle different types of attacks efficiently through building various types of intrusion detection systems capable to handle different types of attacks, known and unknown (novel) ones as well as have the capability to deal with large amount of traffic and data transferring. In this paper, we present an intelligent intrusion detection system based on radial basis function capable to handle all types of attacks and intrusions with high detection accuracy and precision through addressing the intrusion detection problem in the framework of interpolation and adaptive network theories.",
keywords = "Artificial neural network, Clustering, Data approximation, Interpolation, Intrusion detection, Radial basis function",
author = "Alia Abughazleh and Muder Almiani and Basel Magableh and Abdul Razaque",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 6th International Conference on Software Defined Systems, SDS 2019 ; Conference date: 10-06-2019 Through 13-06-2019",
year = "2019",
month = jun,
doi = "10.1109/SDS.2019.8768575",
language = "English",
series = "2019 6th International Conference on Software Defined Systems, SDS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "200--208",
booktitle = "2019 6th International Conference on Software Defined Systems, SDS 2019",
address = "United States",
}