The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
A GA-LR wrapper approach for feature selection in network intrusion detection
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Citations
Cites
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 7,715
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
A survey on feature selection methods
Chandrashekar, G., & Sahin, F. (2014) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.11.024 · Cited by 5,189
Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033
A 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 · cited 1×
“This process is repeated until a leaf node is reached which represents the class of the tested instance (Buczak and Guven, 2016).”
BackgroundAnomaly-based network intrusion detection: Techniques, systems and challenges
García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009) · Computers & Security · doi:10.1016/j.cose.2008.08.003 · Cited by 2,085
Efficient Feature Selection via Analysis of Relevance and Redundancy
Yu, L., & Liu, H. (2004) · doi:10.5555/1005332.1044700 · Cited by 1,983
A survey of network anomaly detection techniques
Ahmed, M., Naser Mahmood, A., & Hu, J. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.11.016 · Cited by 1,750
Cited by
Cyber threat detection based on artificial neural networks using event profiles
Lee, J., Kim, J., Kim, I., & Han, K. (2019) · IEEE Access · doi:10.1109/access.2019.2953095 · Cited by 188
“To evaluate the performance comparison with existing methods, we conducted experiments using well-known conventional machine-learning methods such as support vector machine (SVM) [53], k-nearest neighbor (k-NN) [54], random forest (RF) [55], naive Bayes (NB) [56], and decision tree (DT) [57].”
Method
Metadata
- Authors
- Khammassi, C., & Krichen, S.
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 70
- Pages
- 255-277
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W2626468835
- Cited by
- 382
- References
- 55
- FWCI
- 17.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- Khammassi, C., & Krichen, S. (2017). A GA-LR wrapper approach for feature selection in network intrusion detection. Computers & Security. https://doi.org/10.1016/j.cose.2017.06.005
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
