Abstract
Protecting information systems against intruders' attacks requires utilising intrusion detection systems. Over the past several years, many open-source intrusion datasets have been made available so that academics and researchers can analyse and assess various detection classifiers' effectiveness. These datasets are made available with a full complement of illustrative network features. In this research, we investigate the issue of Network Intrusion Detection (NID) by utilising an Internet of Things (IoT) dataset called Bot-IoT to evaluate the detection efficiency and effectiveness of five different Ensemble Learning Classifiers (ELCs). Our experiment's results showed that despite all ELCs recording high classification metric scores, CatBoost emerged as the ELC that performed the best in our experiment in terms of Accuracy, Precision, F1-Score, Training and Test Time.
Original language | English |
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Title of host publication | 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 510-514 |
Number of pages | 5 |
ISBN (Print) | 978-1-6654-9940-8 |
DOIs | |
Publication status | Published - 21 Oct 2022 |
Event | 2022 13th International Conference on Information and Communication Technology Convergence (ICTC): “Accelerating Digital Transformation with ICT Innovation” - Jeju Island, Korea, Democratic People's Republic of Duration: 19 Oct 2022 → 21 Oct 2022 Conference number: 13 https://ieeexplore.ieee.org/xpl/conhome/9952188/proceeding |
Conference
Conference | 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) |
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Abbreviated title | ICTC 2022 |
Country/Territory | Korea, Democratic People's Republic of |
City | Jeju Island |
Period | 19/10/22 → 21/10/22 |
Internet address |
Keywords
- Training
- Measurement
- Network intrusion detection
- Feature extraction
- Recording
- Information and communication technology
- Internet of Things