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Performance enhancement of intrusion detection system using machine learning algorithms with feature selection

conference contribution
posted on 11.05.2022, 23:33 by Anuradha Samkham Raju, MD Mamunur RashidMD Mamunur Rashid, Fariza SabrinaFariza Sabrina
Cybersecurity has emerged as a major concern for individuals and organisations due to digitalisation. As a result, data is growing exponentially making it susceptible to various cyberattacks. Intrusion detection systems are used to effectively detect cyberattacks to achieve cybersecurity. Traditionally, there are many existing IDS models developed using machine learning algorithms for anomaly detection. This study aims to explore the performance of the IDS using tree-based machine learning algorithms with feature selection. The experiment was conducted using three algorithms Decision Tree (DT), Random Forest (RF), and XGBooster (XGB). Each algorithm used five feature selection techniques Information Gain, Pearson Correlation, Chi-square, Principal Component Analysis, and Recursive Feature Elimination. The experiment is carried out with the NSL-KDD dataset. The performance of the models was evaluated using the performance metrics such as accuracy, recall, precision, F1-score, and false positive rate (FPR). Although each model effectively detects intrusion with different feature selection techniques, DT shows the highest performance with Pearson Correlation and achieved 82% accuracy and 0.02 FPR.

History

Start Page

34

End Page

39

Number of Pages

6

Start Date

24/11/2022

Finish Date

26/03/2022

ISSN

2474-1531

ISBN-13

9781665427845

Location

Sydney, New South Wales, Australia

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Name of Conference

31st International Telecommunication Network and Applications Conference (ITNAC)

Parent Title

2021 31st International Telecommunication Networks and Applications Conference, ITNAC 2021