The DOI matches its Crossref record; 126 of the 126 references it cites were found in a registry or an index.
Network intrusion detection system: A systematic study of machine learning and deep learning approaches
Open accessAbstract
The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.
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Cited by
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“IDSs can be implemented with hardware appliances or with 555 special software; sometimes, they combine both systems [69].”
Enhancing intrusion detection: a hybrid machine and deep learning approach
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Metadata
- Authors
- Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., & Ahmad, F.
- Venue
- Transactions on Emerging Telecommunications Technologies
- Publisher
- Wiley
- Published
- 2020-10-16
- Volume
- 32
- Issue
- 1
- ISSN
- 2161-3915 (ISSN-L)
- OpenAlex
- W3093410479
- Cited by
- 1,288
- References
- 126
- FWCI
- 59.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., & Ahmad, F. (2021). Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies. https://doi.org/10.1002/ett.4150
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