Towards an efficient anomaly-based intrusion detection for software-defined networks

Name: Towards an efficient anomaly-based intrusion detection for software-defined networks
Author: Latah, Majd, Toker, L.

İsim	Towards an efficient anomaly-based intrusion detection for software-defined networks
Yazar	Latah, Majd, Toker, L.
Basım Tarihi:	2018-08-24
Basım Yeri	- Institution of Engineering and Technology
Konu	Software defined networking, Network security, Artificial intelligence
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2047-4954
Kayıt Numarası	e772d7dc-4f29-4cd3-9eeb-8206a50fd469
Tarih	2018-08-24
Örnek Metin	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

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Towards an efficient anomaly-based intrusion detection for software-defined networks

Yazar Latah, Majd, Toker, L.

Basım Tarihi 2018-08-24

Basım Yeri - Institution of Engineering and Technology

Konu Software defined networking, Network security, Artificial intelligence

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2047-4954

Kayıt Numarası e772d7dc-4f29-4cd3-9eeb-8206a50fd469

Tarih 2018-08-24

Örnek Metin 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