- Title
- Adaptive machine learning based network intrusion detection
- Creator
- Chindove, Hatitye E, Brown, Dane L
- Subject
- To be catalogued
- Date
- 2021
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/464052
- Identifier
- vital:76471
- Identifier
- xlink:href="https://doi.org/10.1145/3487923.3487938"
- 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 analyses of dataset balancing and sampling, feature engineering and systematic ML-based NIDS model tuning focused on the adaptive improvement of intrusion detection. A comparison between machine learning classifiers forms part of the evaluation process. Results on the proposed approach model effectiveness for NIDS are discussed. Recurrent neural networks and random forests models consistently achieved high f1-score results with macro f1-scores of 0.73 and 0.87 for the CICIDS 2017 dataset; and 0.73 and 0.72 against the CICIDS 2018 dataset, respectively.
- Format
- computer, online resource, application/pdf, 1 online resource (5 pages), pdf
- Publisher
- ACM Digital Library
- Language
- English
- Relation
- Proceedings of the International Conference on Artificial Intelligence and its Applications, Chindove, H. and Brown, D., 2021, December. Adaptive machine learning based network intrusion detection. In Proceedings of the International Conference on Artificial Intelligence and its Applications (pp. 1-6), Proceedings of the International Conference on Artificial Intelligence and its Applications p. 1 2021 978-1-4503-8575-6
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the ACM Digital Library Statement (https://libraries.acm.org/digital-library/policies#anchor3)
- Rights
- Open Access
- Hits: 56
- Visitors: 55
- 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 machine learning based network intrusion detection.pdf | 469 KB | Adobe Acrobat PDF | View Details |