- Title
- Adaptive network intrusion detection using optimised machine learning models
- Creator
- Chindove, Hatitye E, Brown, Dane L
- Subject
- To be catalogued
- Date
- 2021
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/465634
- Identifier
- vital:76627
- Identifier
- xlink:href="https://www.researchgate.net/publication/358046953_Adaptive_Network_Intrusion_Detection_using_Optimised_Machine_Learning_Models"
- Description
- Network intrusion detection system (NIDS) adoption is essential for mitigating computer network attacks in various scenarios. However, the increasing complexity of computer networks and attacks make it challenging to classify network traffic. Machine learning (ML) techniques in a NIDS can be affected by different scenarios, and thus the recency, size and applicability of datasets are vital factors to consider when selecting and tuning a machine learning classifier. The proposed approach evaluates relatively new datasets constructed such that they depict real-world scenarios. It includes empirical analyses of practical, systematic ML-based NIDS with significant network traffic for improved intrusion detection. A comparison between machine learning classifiers, including deep learning, form part of the evaluation process. Results on how the proposed approach increased model effectiveness for NIDS in a more practical setting are discussed. Recurrent neural networks and random forests models consistently achieved the best results.
- Format
- computer, online resource, application/pdf, 1 online resource (6 pages), pdf
- Publisher
- Use of this resource is governed by the terms and conditions of Southern Africa Telecommunication Networks and Applications Conference (SA TNAC) Statement (https://www.satnac.org.za/)
- Language
- English
- Relation
- Southern Africa telecommunication networks and applications conference, Chindove, H. and Brown, D., 2021. Adaptive network intrusion detection using optimised machine learning models. In Southern Africa telecommunication networks and applications conference (pp. 1-6), Southern Africa telecommunication networks and applications conference 2021
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of Southern Africa Telecommunication Networks and Applications Conference (SA TNAC) Statement (https://www.satnac.org.za/)
- Rights
- Closed Access
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- Visitors: 41
- Downloads: 0
Collections
Brown, Dane A (Dr)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | Adaptive network intrusion detection using optimised machine learning models.pdf | 612 KB | Adobe Acrobat PDF | View Details |