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Performance enhancement of intrusion detection system using bagging ensemble technique with feature selection

conference contribution
posted on 24.06.2021, 23:50 by MD Mamunur RashidMD Mamunur Rashid, Joarder Kamruzzaman, Mohiuddin Ahmed, Nahina IslamNahina Islam, Santoso WibowoSantoso Wibowo, Steven GordonSteven Gordon
An intrusion detection system’s (IDS) key role is to recognise anomalous activities from both inside and outside the network system. In literature, many machine learning techniques have been proposed to improve the performance of IDS. To create a good IDS, a single classifier might not be powerful enough. To overcome this bottleneck researchers focus on hybrid/ensemble techniques. Such methods are more complex and computation intensive, but they provide greater accuracy and lower false alarm rates (FAR). In this paper, we propose a bagging ensemble that improves the performance of IDS in terms of accuracy and FAR where the NSL-KDD dataset has been used to classify benign and abnormal traffic. We have also applied the information gain-based feature selection method to select highly relevant features for improving the accuracy of the proposed technique and achieved 84.93 % accuracy and 2.45 % FAR on the test dataset.

History

Start Page

1

End Page

5

Number of Pages

5

Start Date

16/12/2020

Finish Date

18/12/2020

ISBN-13

9781665419741

Location

Gold Coast, Qld., Australia

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Federation University; Edith Cowan University

Era Eligible

Yes

Name of Conference

IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2020)

Parent Title

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)