Author
Latah, Majd, Toker, L.
Publication Date
2018-08-24
Publication Place
-
Institution of Engineering and Technology
Subject
Software defined networking, Network security, Artificial intelligence
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2047-4954
Record ID
e772d7dc-4f29-4cd3-9eeb-8206a50fd469
Date
2018-08-24
Sample Text
Software-defined networking (SDN) is a new paradigm that allows developing more flexible network applications. A SDN controller, which represents a centralised controlling point, is responsible for running various network applications as well as maintaining different network services and functionalities. Choosing an efficient intrusion detection system helps in reducing the overhead of the running controller and creates a more secure network. In this study, we investigate the performance of the well-known anomaly-based intrusion detection approaches in terms of accuracy, false alarm rate, precision, recall, f1-measure, area under receiver operator characteristic curve, execution time and McNemar's test. Precisely, the authors focus on supervised machine-learning approaches where we use the following classifiers: decision trees, extreme learning machine, Naive Bayes, linear discriminant analysis, neural networks, support vector machines, random forest, K-nearest-neighbour, AdaBoost, RUSBoost, LogitBoost and BaggingTrees where we employ the well-known NSL-KDD benchmark dataset to compare the performance of each one of these classifiers.
DOI
10.1049/iet-net.2018.5080
