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A novel hybrid KPCA and SVM with GA model for intrusion detection
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Citations
Cited by
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
Yin, C., Zhu, Y., Fei, J., & He, X. (2017) · IEEE Access · doi:10.1109/access.2017.2762418 · Cited by 2,100
A novel SVM-kNN-PSO ensemble method for intrusion detection system
Aburomman, A. A., & Ibne Reaz, M. B. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2015.10.011 · Cited by 490
Application of deep learning to cybersecurity: A survey
Mahdavifar, S., & Ghorbani, A. A. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.056 · Cited by 325
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection
Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3014929 · Cited by 282
From intrusion detection to attacker attribution: A comprehensive survey of unsupervised methods
Nisioti, A., Mylonas, A., Yoo, P. D., & Katos, V. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2854724 · Cited by 212
Metadata
- Authors
- Kuang, F., Xu, W., & Zhang, S.
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 18
- Pages
- 178-184
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W2097034581
- Cited by
- 424
- References
- 0
- FWCI
- 29.28
- Type
- journal article
- Language
- en
- Open access
- closed
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How to cite
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- CoChat. (2026). Citation check: A novel hybrid KPCA and SVM with GA model for intrusion detection. https://app.cochat.ai/papers/10.1016/j.asoc.2014.01.028
- The paper
- Kuang, F., Xu, W., & Zhang, S. (2014). A novel hybrid KPCA and SVM with GA model for intrusion detection. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2014.01.028
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.
