Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8073 LNAI, Issue , 2013, Pages 103-111

  De La Hoz, Eduardo ^a,c   Ortiz, Andrés ^b   Ortega, Julio ^a   De La Hoz, Emiro ^a,c  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF MÁLAGA   (Spain)

^c UNIVERSIDAD DE LA COSTA   (Colombia)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION RATES; DIMENSIONALITY REDUCTION TECHNIQUES; INTRUSION DETECTION SYSTEMS; NETWORK ANOMALY DETECTION; NETWORK INTRUSIONS; NONLINEAR PROJECTIONS; SUPPORT VECTOR CLASSIFIERS; SUPPORT VECTOR CLASSIFIERS ENSEMBLE;

CLASSIFICATION (OF INFORMATION); INTELLIGENT SYSTEMS; INTRUSION DETECTION;

COMPUTER SYSTEM FIREWALLS;

EID: 84884934354     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40846-5_11     Document Type: Conference Paper

References (18)

1. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Computing Surveys 41(3) (2009)
2. Hoffman, A., Schimitz, C., Sick, B.: Intrussion detection in computer networks with neural and fuzzy classifiers. In: Kaynak, O., Alpayd?n, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 316-324. Springer, Heidelberg (2003)
3. Network Security Lab - Knowledge Discovery and Data Mining (NSL-KDD) (2007), http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
4. Tavallaee, M., Stakhanova, N., Ghorbani, A.: Toward credible evaluation of anomaly-based intrusion-detection methods. Trans. Sys. Man Cyber Part C 40, 516-524 (2010)
5. Kayacik, H., Zincir-Heywood, A., Heywood, M.: A hierarchical som-based intrusion detection system. Journal Engineering Applications of Artificial Intelligence 20(4), 439-451 (2007)
6. Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.: A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data. In: Applications of Data Mining in Computer Security. Kluwer (2002)
7. Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press (2009)
8. Müller, K., Mika, S., Ratsch, G., Tsuda, B., Schölkopf, B.: An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12(2), 181-201 (2003)
9. Tenenbaum, J., de Silva, V., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319-2323 (2000)
10. Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71-86 (1992)
11. Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience (1998)
12. Bottou, L., Cortes, C., Denker, J., Drucker, H., Guyon, I., Jackel, L., LeCun, Y., Muller, U., Sackinger, E., Simard, P., Vapnik, V.: Comparison of classifier methods: A case study in handwriting digit recognition. In: Proc. International Conference on Pattern Recognition, pp. 77-87 (1994)
13. Bredensteiner, E., Bennett, K.: Multicategory classification by support vector machines. Computational Optimization and Applications 12(1-3), 53-79 (1999)
14. Lippmann, R., Fried, D., Graf, I., Haines, J., Kendball, K., McClung, D., Weber, D.,Webster, S., Wyschgrod, D., Cuningham, R., Zissman, M.: Evaluating intrusion detection systems: the 1998 darpa off-line intrusion detection evaluation. Descex 2, 1012-1027 (2000)
15. McHugh, J.: Testing intrusion detection systems: a critique of the 1998 and 1999 darpa instrusion detection systems evaluation as performed by lyncoln laboratory. ACM Transactions on Information and Systems Security 3(4), 262-294 (2000)
16. Panda, M., Abraham, A., Abraham, M.: Discriminative multinomial naïve bayes for network intrusion detection. In: 6th Conference on Information Assurance and Security, IAS (2010)
17. Nziga, J.: Minimal dataset for network intrusion detection systems via dimensionality reduction. In: 6th International Conference on Digital Information Management, ICDIM (2011)
18. Tavallaee, M., Bagheri, E., Wei, L., Ghorbani, A.: A detailed analysis of the kddcup 1999 dataset. In: Proceedings of the IEEE International Symposium on Computational Intelligence in Security and Defense Applications CISDA (2009)