The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
An efficient intrusion detection system based on support vector machines and gradually feature removal method
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Cites
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,947
Intrusion detection by machine learning: A review
Tsai, C.-F., Hsu, Y.-F., Lin, C.-Y., & Lin, W.-Y. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.05.029 · Cited by 969
A clustering technique for summarizing multivariate data
Ball, G. H., & Hall, D. J. (1967) · Behavioral Science · doi:10.1002/bs.3830120210 · Cited by 912
The use of computational intelligence in intrusion detection systems: A review
Wu, S. X., & Banzhaf, W. (2010) · Applied Soft Computing · doi:10.1016/j.asoc.2009.06.019 · Cited by 696
Feature deduction and ensemble design of intrusion detection systems
Chebrolu, S., Abraham, A., & Thomas, J. P. (2005) · Computers & Security · doi:10.1016/j.cose.2004.09.008 · Cited by 604
Depren, O., Topallar, M., Anarim, E., & Ciliz, M. K. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.05.002 · Cited by 513
A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering
Wang, G., Hao, J., Ma, J., & Huang, L. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.02.102 · Cited by 505
Training v-Support Vector Classifiers: Theory and Algorithms
Chang, C.-C., & Lin, C.-J. (2001) · Neural Computation · doi:10.1162/089976601750399335 · Cited by 499
Naive Bayes vs decision trees in intrusion detection systems
Amor, N. B., Benferhat, S., & Elouedi, Z. (2004) · Proceedings of the 2004 ACM symposium on Applied computing · doi:10.1145/967900.967989 · Cited by 479
Cited by
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
Survey of intrusion detection systems: Techniques, datasets and challenges
Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019) · Cybersecurity · doi:10.1186/s42400-019-0038-7 · Cited by 2,024
Cybersecurity data science: an overview from machine learning perspective
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00318-5 · Cited by 766
IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model
Sarker, I. H., Abushark, Y. B., Alsolami, F., & Khan, A. I. (2020) · Symmetry · doi:10.3390/sym12050754 · Cited by 356
Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265
Metadata
- Authors
- Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X., & Dai, K.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 39
- Issue
- 1
- Pages
- 424-430
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W1978779053
- Cited by
- 386
- References
- 20
- FWCI
- 16.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X., & Dai, K. (2012). An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2011.07.032
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.
