1. Tavallaee, M., Bagheri, E., Lu, W. and Ghorbani, A.A., "A detailed analysis of the kdd cup 99 data set", in Computational Intelligence for Security and Defense Applications. CISDA. IEEE Symposium on, IEEE., (2009), 1-6.
2. Jain, A.K., Duin, R.P.W. and Mao, J., "Statistical pattern recognition: A review", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, (2000), 4-37.
3. Trunk, G.V., "A problem of dimensionality: A simple example", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol., No. 3, (1979), 306-307.
4. Erfani, S.M., Rajasegarar, S., Karunasekera, S. and Leckie, C., "High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning", Pattern Recognition, Vol. 58, (2016), 121-134.
5. Wang, W., Guyet, T., Quiniou, R., Cordier, M.-O., Masseglia, F. and Zhang, X., "Autonomic intrusion detection: Adaptively detecting anomalies over unlabeled audit data streams in computer networks", Knowledge-Based Systems, Vol. 70, (2014), 103-117.
6. Kuang, F., Xu, W. and Zhang, S., "A novel hybrid kpca and svm with ga model for intrusion detection", Applied Soft Computing, Vol. 18, (2014), 178-184.
7. de la Hoz, E., Ortiz, A., Ortega, J. and de la Hoz, E., "Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques", in International Conference on Hybrid Artificial Intelligence Systems, Springer., (2013), 103-111.
8. Lakhina, S., Joseph, S. and Verma, B., "Feature reduction using principal component analysis for effective anomaly–based intrusion detection on nsl-kdd", (2010).
9. Sindhu, S.S.S., Geetha, S. and Kannan, A., "Decision tree based light weight intrusion detection using a wrapper approach", Expert Systems with Applications, Vol. 39, No. 1, (2012), 129-141.
10. Kim, G., Lee, S. and Kim, S., "A novel hybrid intrusion detection method integrating anomaly detection with misuse detection", Expert Systems with Applications, Vol. 41, No. 4, (2014), 1690-1700.
11. Saied, A., Overill, R.E. and Radzik, T., "Detection of known and unknown ddos attacks using artificial neural networks", Neurocomputing, Vol. 172, (2016), 385-393.
12. Wang, G., Hao, J., Ma, J. and Huang, L., "A new approach to intrusion detection using artificial neural networks and fuzzy clustering", Expert Systems with Applications, Vol. 37, No. 9, (2010), 6225-6232.
13. Meng, W., Li, W. and Kwok, L.-F., "Efm: Enhancing the performance of signature-based network intrusion detection systems using enhanced filter mechanism", Computers & Security, Vol. 43, (2014), 189-204.
14. Lin, W.-C., Ke, S.-W. and Tsai, C.-F., "Cann: An intrusion detection system based on combining cluster centers and nearest neighbors", Knowledge-Based Systems, Vol. 78, (2015), 13-21.
15. Bukhtoyarov, V. and Zhukov, V., "Ensemble-distributed approach in classification problem solution for intrusion detection systems", in International Conference on Intelligent Data Engineering and Automated Learning, Springer., (2014), 255-265.
16. Catania, C.A., Bromberg, F. and Garino, C.G., "An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection", Expert Systems with Applications, Vol. 39, No. 2, (2012), 1822-1829.
17. Li, W., "Using genetic algorithm for network intrusion detection", Proceedings of the United States Department of Energy Cyber Security Group, Vol. 1, (2004), 1-8.
18. Fidelis, M.V., Lopes, H.S. and Freitas, A.A., "Discovering comprehensible classification rules with a genetic algorithm", in Evolutionary Computation. Proceedings of the 2000 Congress on, IEEE. Vol. 1, (2000), 805-810.
19. De la Hoz, E., De La Hoz, E., Ortiz, A., Ortega, J. and Prieto, B., "Pca filtering and probabilistic som for network intrusion detection", Neurocomputing, Vol. 164, (2015), 71-81.
20. De la Hoz, E., de la Hoz, E., Ortiz, A., Ortega, J. and Martínez-Álvarez, A., "Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps", Knowledge-Based Systems, Vol. 71, (2014), 322-338.
21. Shafi, K. and Abbass, H.A., "An adaptive genetic-based signature learning system for intrusion detection", Expert Systems with Applications, Vol. 36, No. 10, (2009), 12036-12043.
22. Tsang, C.-H., Kwong, S. and Wang, H., "Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection", Pattern Recognition, Vol. 40, No. 9, (2007), 2373-2391.
23. Eesa, A.S., Orman, Z. and Brifcani, A.M.A., "A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems", Expert Systems with Applications, Vol. 42, No. 5, (2015), 2670-2679.
24. Fossaceca, J.M., Mazzuchi, T.A. and Sarkani, S., "Mark-elm: Application of a novel multiple kernel learning framework for improving the robustness of network intrusion detection", Expert Systems with Applications, Vol. 42, No. 8, (2015), 4062-4080.
25. Bamakan, S.M.H., Wang, H., Yingjie, T. and Shi, Y., "An effective intrusion detection framework based on mclp/svm optimized by time-varying chaos particle swarm optimization", Neurocomputing, Vol. 199, (2016), 90-102.
26. Bhuyan, M.H., Bhattacharyya, D. and Kalita, J.K., "A multi-step outlier-based anomaly detection approach to network-wide traffic", Information Sciences, Vol. 348, (2016), 243-271.
27. Amiri, F., Yousefi, M.R., Lucas, C., Shakery, A. and Yazdani, N., "Mutual information-based feature selection for intrusion detection systems", Journal of Network and Computer Applications, Vol. 34, No. 4, (2011), 1184-1199.
28. Sangkatsanee, P., Wattanapongsakorn, N. and Charnsripinyo, C., "Practical real-time intrusion detection using machine learning approaches", Computer Communications, Vol. 34, No. 18, (2011), 2227-2235.
29. Pereira, C.R., Nakamura, R.Y., Costa, K.A. and Papa, J.P., "An optimum-path forest framework for intrusion detection in computer networks", Engineering Applications of Artificial Intelligence, Vol. 25, No. 6, (2012), 1226-1234.