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A tree-based stacking ensemble technique with feature selection for network intrusion detection

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posted on 2023-01-25, 01:09 authored by MD Mamunur RashidMD Mamunur Rashid, Joarder Kamruzzaman, Tasadduq ImamTasadduq Imam, Santoso WibowoSantoso Wibowo, Steven GordonSteven Gordon
Several studies have used machine learning algorithms to develop intrusion systems (IDS), which differentiate anomalous behaviours from the normal activities of network systems. Due to the ease of automated data collection and subsequently an increased size of collected data on network traffic and activities, the complexity of intrusion analysis is increasing exponentially. A particular issue, due to statistical and computation limitations, a single classifier may not perform well for large scale data as existent in modern IDS contexts. Ensemble methods have been explored in literature in such big data contexts. Although more complicated and requiring additional computation, literature has a note that ensemble methods can result in better accuracy than single classifiers in different large scale data classification contexts, and it is interesting to explore how ensemble approaches can perform in IDS. In this research, we introduce a tree-based stacking ensemble technique (SET) and test the effectiveness of the proposed model on two intrusion datasets (NSL-KDD and UNSW-NB15). We further enhance incorporate feature selection techniques to select the best relevant features with the proposed SET. A comprehensive performance analysis shows that our proposed model can better identify the normal and anomaly traffic in network than other existing IDS models. This implies the potentials of our proposed system for cybersecurity in Internet of Things (IoT) and large scale networks.

History

Volume

52

Issue

9

Start Page

9768

End Page

9781

Number of Pages

14

eISSN

1573-7497

ISSN

0924-669X

Publisher

Springer Link

Language

en

Peer Reviewed

Yes

Open Access

No

Acceptance Date

2021-11-01

Era Eligible

Yes

Journal

Applied Intelligence