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All issues Volume 44 (2016) MATEC Web of Conferences, 44 (2016) 02041 References
Open Access
Issue
MATEC Web of Conferences
Volume 44, 2016
2016 International Conference on Electronic, Information and Computer Engineering
Article Number 02041
Number of page(s) 7
Section Electronics, Information and Engineering Application
DOI https://doi.org/10.1051/matecconf/20164402041
Published online 08 March 2016
  1. H.J. Liao, C.H.R. Lin, Y.C. Lin, K.Y. Tung, Intrusion detection system: A comprehensive review, JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 36, 1: 16–24 (2013). [CrossRef] [Google Scholar]
  2. M.S. Abadeh, J. Habibi, C. Lucas, Intrusion detection using a fuzzy genetics-based learning algorithm, Journal of Network and Computer Applications, 30, 1: 414–428 (2007) [CrossRef] [Google Scholar]
  3. A. Tajbakhsh, M. Rahmati, A. Mirzaei, Intrusion detection using fuzzy association rules, Applied Soft Computing Journal, 9, 2: 462–469 (2009) [CrossRef] [Google Scholar]
  4. Y.H. Liao, V.R. Vemuri, Use of k-nearest neighbor classifier for intrusion detection, COMPUTERS & SECURITY, 21, 5: 439–448 (2002). [CrossRef] [Google Scholar]
  5. M.-Y. Su, Real-time anomaly detection systems for denial-of-service attacks by weighted k-nearest-neighbor classifiers, Expert Systems With Applications, 38, 4: 3492–3498 (2011) [CrossRef] [Google Scholar]
  6. S. Mukkamala, A.H. Sung, A. Abraham, Intrusion detection using an ensemble of intelligent paradigms, Journal of Network and Computer Applications, 28, 2: 167–182 (2005) [Google Scholar]
  7. S. Mukkamala, A.H. Sung, Feature ranking and selection for intrusion detection systems using support vector machines. In International conference on information and knowledge engineering (ICIKE). 503–509 (2002) [Google Scholar]
  8. D. Fisch, A. Hofmann, B. Sick, On the versatility of radial basis function neural networks: A case study in the field of intrusion detection, Information Sciences, 180, 12: 2421–2439 (2010) [CrossRef] [Google Scholar]
  9. Z.A. Baig, S.M. Sait, A. Shaheen, Gmdh-based networks for intelligent intrusion detection, ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 26, 7: 1731–1740 (2013) [CrossRef] [Google Scholar]
  10. S.S.S. Sindhu, S. Geetha, A. Kannan, Decision tree based light weight intrusion detection using a wrapper approach, EXPERT SYSTEMS WITH APPLICATIONS, 39, 1: 129–141 (2012) [Google Scholar]
  11. A.S. Eesa, Z. Orman, A.M.A. Brifcani, A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems, EXPERT SYSTEMS WITH APPLICATIONS 42, 5: 2670–2679 (2015) [CrossRef] [Google Scholar]
  12. T. Shon, X. Kovah, J. Moon, Applying genetic algorithm for classifying anomalous tcp/ip packets, Neurocomputing, 69, 16: 2429–2433 (2006) [CrossRef] [Google Scholar]
  13. D. lppoliti, X.B. Zhou, A-ghsom: An adaptive growing hierarchical self organizing map for network anomaly detection, JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 72, 12: 1576–1590 (2012) [CrossRef] [Google Scholar]
  14. N. Ye, Y. Zhang, C.M. Borror, Robustness of the markov-chain model for cyber-attack detection, IEEE Transactions on Reliability, 53, 1: 116–123 (2004) [CrossRef] [Google Scholar]
  15. S. Shin, S. Lee, H. Kim, S. Kim, Advanced probabilistic approach for network intrusion forecasting and detection, EXPERT SYSTEMS WITH APPLICATIONS, 40, 1: 315–322 (2013) [CrossRef] [Google Scholar]
  16. T. Shon, J. Moon, A hybrid machine learning approach to network anomaly detection, Information Sciences, 177, 18: 3799–3821 (2007) [CrossRef] [Google Scholar]
  17. R.M. Elbasiony, E.A. Sallam, T.E. Eltobely, M.M. Fahmy, A hybrid network intrusion detection framework based on random forests and weighted k-means, Ain Shams Engineering Journal, 4, 4: 753–762 (2013) [CrossRef] [Google Scholar]
  18. G. Liu, Z. Yi, S. Yang, A hierarchical intrusion detection model based on the pca neural networks, Neurocomputing, 70, 7: 1561–1568 (2007) [CrossRef] [Google Scholar]
  19. Significant feature selection using computational intelligent techniques for intrusion detection. London: Springer London. 285–306 (2005) [Google Scholar]
  20. R. Datti, S. Lakhina, Performance comparison of features reduction techniques for intrusion detection system, International Journal of Compute Science and Technology, 3, 1: 332–335 (2012) [Google Scholar]
  21. C.-F. Tsai, C.-Y. Lin, A triangle area based nearest neighbors approach to intrusion detection, Pattern Recognition, 43, 1: 222–229 (2010) [CrossRef] [Google Scholar]
  22. C. Guo, Y. Zhou, Y. Ping, Z. Zhang, G. Liu, Y. Yang, A distance sum-based hybrid method for intrusion detection, Applied Intelligence, 40, 1: 178-188 (2014) [Google Scholar]
  23. W.C. Lin, S.W. Ke, C.F. Tsai, Cann: An intrusion detection system based on combining cluster centers and nearest neighbors, KNOWLEDGE-BASED SYSTEMS, 78: 13–21 (2015) [CrossRef] [Google Scholar]
  24. W. Hong, X. Li, The new graphical features of star plot for k nearest neighbor classifier. 4682, Berlin, Heidelberg: Springer Berlin Heidelberg. 926–933 (2007) [Google Scholar]
  25. J. Parkinson, M. Blaxter, Simitri - visualizing similarity relationshipsforgroupsofsequences, BIOINFORMATICS, 19, 3: 390–395 (2003) [CrossRef] [Google Scholar]
  26. B. Luo, J.B. Xia, A novel intrusion detection system based on feature generation with visualization strategy, EXPERT SYSTEMS WITH APPLICATIONS, 41, 9: 4139–4147 (2014) [CrossRef] [Google Scholar]

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