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Identifying important features for intrusion detection using support vector machines and neural networks
Abstract
Intrusion detection is a critical component of secure information systems. This paper addresses the issue of identifying important input features in building an intrusion detection system (IDS). Since elimination of the insignificant and/or useless inputs leads to a simplification of the problem, faster and more accurate detection may result. Feature ranking and selection, therefore, is an important issue in intrusion detection. We apply the technique of deleting one feature at a time to perform experiments on SVMs and neural networks to rank the importance of input features for the DARPA collected intrusion data. Important features for each of the 5 classes of intrusion patterns in the DARPA data are identified. It is shown that SVM-based and neural network based IDSs using a reduced number of features can deliver enhanced or comparable performance. An IDS for class-specific detection based on five SVMs is proposed.
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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
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Artificial intelligence, cyber-threats and industry 4.0: challenges and opportunities
Bécue, A., Praça, I., & Gama, J. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09942-2 · Cited by 368
Toward Credible Evaluation of Anomaly-Based Intrusion-Detection Methods
Tavallaee, M., Stakhanova, N., & Ghorbani, A. A. (2010) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2048428 · Cited by 280
SALMON: survival analysis learning with multi-omics neural networks on breast cancer
Huang, Z., Zhan, X., Xiang, S., Johnson, T. S., Helm, B., Yu, C. Y., Zhang, J., Salama, P., Rizkalla, M., Han, Z., et al. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00166 · Cited by 262
“This approach is widely adopted for feature selection and ranking the importance of features in neural network (Setiono and Liu, 1997; Zhang, 2000; Sung and Mukkamala, 2003).”
BackgroundAzam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265
Metadata
- Authors
- Sung, A. H., & Mukkamala, S.
- Venue
- 2003 Symposium on Applications and the Internet, 2003. Proceedings.
- Publisher
- IEEE Comput. Soc
- Published
- 2003-08-27
- Pages
- 209-216
- OpenAlex
- W2130676130
- Cited by
- 384
- References
- 1
- FWCI
- 6.68
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Sung, A. H., & Mukkamala, S. (2003). Identifying important features for intrusion detection using support vector machines and neural networks. 2003 Symposium on Applications and the Internet, 2003. Proceedings. https://doi.org/10.1109/saint.2003.1183050
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