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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

A Performance Analysis Approach for Network Intrusion Detection Algorithms

verfasst von : Zhihao Wang, Dingde Jiang, Yuqing Wang, Junyang Zhang

Erschienen in: Simulation Tools and Techniques

Verlag: Springer International Publishing

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Abstract

With the development of mobile Internet and cloud computing, the amount of network traffic has been significantly increased. Security problems have drawn a lot of attention, while traditional methods are becoming increasingly unsuitable for it. In this paper, three machine learning algorithms are employed to detect network intrusion, including KNN, Random Forest, and Multilayer Perceptron. Performance evaluation and comparison between them are conducted, in terms of precision, recall, training time, etc. Simulation results on the NSL-KDD, a benchmark data set of network intrusion detection, show that the Random Forest algorithm exhibits higher detection accuracy and remarkably shorter training time.

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Vorheriges Kapitel A Network Traffic Measurement Approach in Cloud-Edge SDN Networks
Nächstes Kapitel On Exponential Stability for Delayed Inertial BAM Neural Networks via Non-reduced Order Approach
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Metadaten
Titel
A Performance Analysis Approach for Network Intrusion Detection Algorithms
verfasst von
Zhihao Wang
Dingde Jiang
Yuqing Wang
Junyang Zhang
Copyright-Jahr
2021
Buch
Simulation Tools and Techniques

Print ISBN: 978-3-030-72791-8

Electronic ISBN: 978-3-030-72792-5

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-72792-5_20