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Intrusion detection using fuzzy association rules
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Research works printed but not linked: 13
- Arslan, 2001, Determination of fuzzy logic membership functions using genetic algorithms, Fuzzy Sets Syst., 118, 297, 10.1016/S0165-0114(99)00065-2 · doi:10.1016/s0165-0114(99)00065-2
- Biermann, 2001, A comparison of Intrusion Detection Systems, Comput. Security, 20, 676, 10.1016/S0167-4048(01)00806-9 · DOI
- C. Borgelt, Efficient implementation of Apriori and Elact, Presented at Workshop of Frequent Item Set Mining Implementations FIMI, USA, 2003.
- C. Borgelt, R. Kruse, Induction of association rules: Apriori implementation, Presented at 15th Conference on Computational Statistics (Germany, 2002). Available: http://fuzzy.cs.uni-magdeburg.de/∼borgelt/papers/cstat_02.pdf. · DOI · Paper page
- Han, 2003, Detecting intrusion with rule-based integration of multiple models, Comput. Security, 22, 613, 10.1016/S0167-4048(03)00711-9 · DOI
- Kuok, 1998, Mining fuzzy association rules in databases, SIGMOD Record, 27, 41, 10.1145/273244.273257 · DOI · Paper page
- Li, 2002, Mining the optimal Class Association Rule set, Knowledge Based Syst., 15, 399, 10.1016/S0950-7051(02)00024-2 · DOI
- Lippmann, 2000, Improving intrusion detection performance using keyword selection and neural networks, Comput. Netw., 34, 594, 10.1016/S1389-1286(00)00140-7 · DOI · Paper page
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- S.J. Stolfo, et al., 1999, KDD-99 dataset, Available: http://www.kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
- A. Tajbakhsh, Design and implementation of an Intrusion Detection Systems using data mining techniques, M.Sc. Thesis, Instructor M. Rahmati, Department of computer engineering, Amirkabir University of Technology, Iran, 2006.
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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
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392
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
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00765-8 · Cited by 570
Decision Tree Based Intrusion Detection System for NSL-KDD Dataset
Ingre, B., Yadav, A., & Soni, A. K. (2018) · Smart Innovation, Systems and Technologies · doi:10.1007/978-3-319-63645-0_23 · Cited by 150
Metadata
- Authors
- Tajbakhsh, A., Rahmati, M., & Mirzaei, A.
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2009-03
- Volume
- 9
- Issue
- 2
- Pages
- 462-469
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W2123619513
- Cited by
- 242
- References
- 0
- FWCI
- 20.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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- Tajbakhsh, A., Rahmati, M., & Mirzaei, A. (2009). Intrusion detection using fuzzy association rules. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2008.06.001
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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.
