The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A novel hybrid intrusion detection method integrating anomaly detection with misuse detection
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
Research works printed but not linked: 11
- Barbara, D., Couto, J., Jajodia, S., Popyack, L., & Wu, N. (2001). ADAM: Detecting intrusions by data mining. In Proceedings of the IEEE workshop on information assurance and security (pp. 11–16).
- Chang, 2010, Tree decomposition for large-scale SVM problems, Journal of Machine Learning Research, 10, 2935
- Depren, 2005, An intelligent intrusion detection system for anomaly and misuse detection in computer networks, Expert Systems with Applications, 29, 713, 10.1016/j.eswa.2005.05.002 · DOI · Paper page
- Hwang, 2007, Hybrid intrusion detection with weighted signature generation over anomalous Internet episodes, IEEE Transactions on Dependable and Secure Computing, 4, 41, 10.1109/TDSC.2007.9 · DOI
- Manevitz, 2002, One-class SVMs for document classification, Journal of Machine Learning Research, 2, 139
- Perdisci, R., Gu, G., & Lee, W. (2006). Using an ensemble of one-class SVM classifiers to harden payload-based anomaly detection systems. In Proceedings of the 6th international conference on data mining (pp. 488–498). · DOI · Paper page
- Quinlan, 1996, Learning decision tree classifier, ACM Computing Surveys (CSUR), 28, 71, 10.1145/234313.234346 · DOI · Paper page
- Shin, 2005, One-class support vector machines: An application in machine fault detection and classification, Computers & Industrial Engineering, 48, 395, 10.1016/j.cie.2005.01.009 · DOI · Paper page
- Song, 2009, Unsupervised anomaly detection based on clustering and multiple one-class SVM, IEICE Transactions on Communications, E92-B, 1982, 10.1587/transcom.E92.B.1981 · DOI
- Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. (2009). A detailed analysis of the KDD CUP 99 data set. In Proceeding of the IEEE symposium on computational intelligence for security and defense applications (pp. 53–58). · DOI · Paper page
- Zhang, J., & Zulkernine, M. (2006). A hybrid network intrusion detection technique using random forests. In Proceedings of the first international conference on availability, reliability and security (pp. 262–269). · DOI
Cited by
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
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
Machine Learning for Anomaly Detection: A Systematic Review
Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021) · IEEE Access · doi:10.1109/access.2021.3083060 · Cited by 748
A GA-LR wrapper approach for feature selection in network intrusion detection
Khammassi, C., & Krichen, S. (2017) · Computers & Security · doi:10.1016/j.cose.2017.06.005 · Cited by 382
Metadata
- Authors
- Kim, G., Lee, S., & Kim, S.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 41
- Issue
- 4
- Pages
- 1690-1700
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2066832805
- Cited by
- 589
- References
- 3
- FWCI
- 30.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Kim, G., Lee, S., & Kim, S. (2014). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2013.08.066
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.
