The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Intrusion detection using neural networks and support vector machines
Abstract
Information security is an issue of serious global concern. The complexity, accessibility, and openness of the Internet have served to increase the security risk of information systems tremendously. This paper concerns intrusion detection. We describe approaches to intrusion detection using neural networks and support vector machines. The key ideas are to discover useful patterns or features that describe user behavior on a system, and use the set of relevant features to build classifiers that can recognize anomalies and known intrusions, hopefully in real time. Using a set of benchmark data from a KDD (knowledge discovery and data mining) competition designed by DARPA, we demonstrate that efficient and accurate classifiers can be built to detect intrusions. We compare the performance of neural networks based, and support vector machine based, systems for intrusion detection.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Cited by
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
Can machine learning be secure?
Barreno, M., Nelson, B., Sears, R., Joseph, A. D., & Tygar, J. D. (2006) · Proceedings of the 2006 ACM Symposium on Information, computer and communications security · doi:10.1145/1128817.1128824 · Cited by 836
Enhanced Network Anomaly Detection Based on Deep Neural Networks
Naseer, S., Saleem, Y., Khalid, S., Bashir, M. K., Han, J., Iqbal, M. M., & Han, K. (2018) · IEEE Access · doi:10.1109/access.2018.2863036 · Cited by 507
Community detection in networks: A multidisciplinary review
Javed, M. A., Younis, M. S., Latif, S., Qadir, J., & Baig, A. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.02.011 · Cited by 499
Metadata
- Authors
- Mukkamala, S., Janoski, G., & Sung, A.
- Venue
- Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)
- Publisher
- IEEE
- Published
- 2003-06-25
- Pages
- 1702-1707
- OpenAlex
- W2120617515
- Cited by
- 814
- References
- 1
- FWCI
- 15.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Mukkamala, S., Janoski, G., & Sung, A. (2003). Intrusion detection using neural networks and support vector machines. Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290). https://doi.org/10.1109/ijcnn.2002.1007774
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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