Intrusion detection based on dynamic self-organizing map neural network clustering

Lecture Notes in Computer Science

Volumn 3498, Issue III, 2005, Pages 428-433

  Feng, Yong ^a   Wu, Kaigui ^a   Wu, Zhongfu ^a   Xiong, Zhongyang ^a  

^a CHONGQING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA ACQUISITION; SELF ORGANIZING MAPS;

DATA CLUSTERS; DYNAMIC SELF-ORGANING MAPS (DSOM); INTRUSION DETECTION; INTRUSION DETECTION ALGORITHM;

NEURAL NETWORKS;

EID: 24944561411     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11427469_69     Document Type: Conference Paper

References (14)

1. Forrest, S., Perelson, A., Allen, L., Cherukury, R.: Self-Nonself Discrimination in a Computer. In Proceeding of the IEEE Symp. on Research in Security and Privacy (1994)
2. Singh, S.: Anomaly Detection Using Negative Selection Based on the Rcontiguous Matching Rule. In 1st International Conference on Artificial Immune Systems (2002) 99-106
3. Lane, T., Brodley, C.E.: An Application of Machine Learning to Anomaly Detection. In Proc. 20th NIST-NCSC National Information Systems Security Conference (1997)
4. Lane, T., Brodley, C.E.: Sequence Matching and Learning in Anomaly Detection for Computer Security. In AI Approaches to Fraud Detection and Risk Management (Fawcett, Haimowitz, Provost, Stolfo, eds.), AAAI Press (1997) 43-49
5. Mahoney, M., Chan, P.: Learning Nonstationary Models of Normal Network Traffic for Detecting Novel Attacks. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002) 23-26
6. Fan, W., Lee, W., Miller, M., Stolfo, S., Chan, P.: Using Artificial Anomalies to Detect Unknown and Known Network Intrusions. In Proceedings of the 1st IEEE International Conference on Data Mining (2001)
7. Gonzalez, F., Dasgupta, D.: Neuro-Immune and Self-Organizing Map Approaches to Anomaly Detection: A Comparison, In 1st International Conference on Artificial Immune Systems (2002)
8. Portnoy, L., Eskin, E., Stolfo, S. J.: Intrusion Detection with Unlabeled Data Using Clustering. In Proceeding of ACM CSS Workshop on Data Mining Applied to Security, Philadelphia, PA (2001)
9. Kohonen, T.: Self-Organizing Maps, Springer-Verlag, Berlin (1995)
10. Alahakoon, L. D., Halgamuge, S. K., Srinivasan, B.: A Structure Adapting Feature Map for Optimal Cluster Representation. In Proc. Int. Conf. Neural Information Processing (1998) 809-812
11. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. High Education Press, Morgan Kaufman Publishers (2001)
12. Duda, R., Hart, P.: Pattern Classification and Scene Analysis. New York: John Wiley & Sons (1973)
13. KDD99: KDD99 Cup Dataset. http://kdd.ics.uci.edu/databases/kddcup99 (1999)
14. Eskin, E., Arnold, A., Prerau, M.: A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data, Published in Data Mining for Security Applications, Kluwer (2002)