Growing recurrent self organizing map

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2007, Pages 290-295

  Yelog̃lu, Özge ^a,b   Nur Zincir Heywood, A ^a,b   Heywood, Malcolm I ^a,b  

^a IEEE

^b DALHOUSIE UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPETITION; COMPUTATIONAL METHODS; DATA MINING; RECURRENT NEURAL NETWORKS;

CAPABILITY OF DETECTION; HIERARCHICAL SOM ARCHITECTURE;

SELF ORGANIZING MAPS;

EID: 40949093164     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2007.4414001     Document Type: Conference Paper

References (15)

1. P. Lichodzijewski. A.N. Zincir-Heywood. M.I. Heywood, "Host-Based Intrusion Detection Using Self-Organizing Maps", IEEE International Joint Conference on Neural Networks, pp. 1714-1719, May 2002.
2. H.G. Kayacik, A.N. Zincir-Heywood, M.I. Heywood, "On the Capability of an SOM based Intrusion Detection System". IEEE-INNS International Joint Conference on Neural Networks, pp. 1808-1813, 2003.
3. S.T. Sarasamma, Q.A. Zhu, J. Huff, "Hierarchical Kohonen Net for Anomaly Detection in Network Security", IEEE Transactions in Systems, Man and Cybernetics, Part B: Cybernetics, vol.35(2), pp. 302-312, April 2005.
4. M. Dittenbach, D. Merkl, A. Rauber, "The Growing Hierarchical Self-Organizing Map", Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 6, pp. 15-19, July 2000.
5. J. Zhou, Y. Fu, "Clustering High-Dimensional Data Using Growing SOM", Advances in Neural Networks ISNN 2005, Springer (2005), vol.3497, pp. 63-68.
6. Y. Liu, D. Tian, B. Li, "A Wireless Intrusion Detection Method Based on Dynamic Growing Neural Network", Computer and Computational Sciences, 2006. IMSCCS '06. First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06), vol. 2, pp. 611-615, April 2006.
7. S. Hettich, S.D. Bay, The UCI KDD Archive, Irvine, CA: University of California. Department of Information and Computer Science, http://kdd.ics.uci.edu. 1999.
8. W. Lee, S. Stolfo, K. Mok, "A Data Mining Framework for Building Intrusion Detection Models", Proceedings of the 1999 IEEE Sypmposium on Security and Privacy, pp. 120-132, 1999.
9. M. Varsta, J. Heikkonnen, J. D. R. Millan, "Context Learning with the Self-Organizing Map", Proceedings of Workshop on Self-Organizing Maps, Helsinki University of Technology, pp. 197-202, 1997.
10. The Intrusion Detection Off-Line Evaluation Plan, MIT Lincoln Lab., Information Systems Technology Group, http://www.ll.mit.edu/IST/ideval/ docs/1998/id98-eval-ll.txt, March 1998.
11. T. Kohonen, Self-Organizing Maps,M Ed., Springer-Verlag. ISBN 3-540-67921-9, 2000.
12. G. J. Chappell, J. G. Taylor, "The Temporal Kohonen Map", Neural Networks, vol. 6, pp. 441-445, 1995.
13. S. Haykin, Neural Networks; 2nd Ed., Prentice Hall. pp. 446, 1999.
14. E. Eskin, A. Arnold, M. Prerau, L. Portnoy, S. Stolfo, "A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data", in Applications of Data Mining in Computer Security, Chapter 4, D. Barbara and S. Jajodia (editors), Kluwer, ISBN 1-4020-7054-3, 2002.
15. J. McHugh, "Testing Intrusion Detection Systems: A Critique of the 1998 and 1999 DARPA Intrusion Detection System Evaluations as Performed by Lincoln Laboratory". ACM Transactions on Information and System Security, 3(4), pp. 262-294, 2001.