The DOI matches its Crossref record; 118 of the 118 references it cites were found in a registry or an index.
A holistic review of Network Anomaly Detection Systems: A comprehensive survey
Abstract
Abstract Network Anomaly Detection Systems (NADSs) are gaining a more important role in most network defense systems for detecting and preventing potential threats. The paper discusses various aspects of anomaly-based Network Intrusion Detection Systems (NIDSs). The paper explains cyber kill chain models and cyber-attacks that compromise network systems. Moreover, the paper describes various Decision Engine (DE) approaches, including new ensemble learning and deep learning approaches. The paper also provides more details about benchmark datasets for training and validating DE approaches. Most of NADSs’ applications, such as Data Centers, Internet of Things (IoT), as well as Fog and Cloud Computing, are also discussed. Finally, we present several experimental explanations which we follow by revealing various promising research directions.
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Cites
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Self-citationA Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
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A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
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Cited by
Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., & Anwar, A. (2020) · IEEE Access · doi:10.1109/access.2020.3022862 · Cited by 892
Self-citationMoustafa, N. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.102994 · Cited by 721
Self-citationMoustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3280465 · Cited by 266
Self-citationDeep Learning-Based Anomaly Detection in Video Surveillance: A Survey
Duong, H.-T., Le, V.-T., & Hoang, V. T. (2023) · Sensors · doi:10.3390/s23115024 · Cited by 178
LITNET-2020: An Annotated Real-World Network Flow Dataset for Network Intrusion Detection
Damasevicius, R., Venckauskas, A., Grigaliunas, S., Toldinas, J., Morkevicius, N., Aleliunas, T., & Smuikys, P. (2020) · Electronics · doi:10.3390/electronics9050800 · Cited by 132
Metadata
- Authors
- Moustafa, N., Hu, J., & Slay, J.
- Venue
- Journal of Network and Computer Applications
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 128
- Pages
- 33-55
- ISSN
- 1084-8045 (ISSN-L)
- OpenAlex
- W2904539465
- Cited by
- 317
- References
- 118
- FWCI
- 19.14
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
