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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Gu, Feng; Greensmith, Julie; Aickelin, Uwe (2010)
Publisher: Springer
Languages: English
Types: Part of book or chapter of book
Subjects: Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, Computer Science - Cryptography and Security
As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performances in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DXA, including the antigen multiplier and moving time windows are reported. The real-valued Negative Selection Algorithm (NSA) using constant-sized detectors and the C4.5 decision tree algorithm are used, to conduct a baseline comparison. The results suggest that the DCA is applicable to KDD 99 data set, and the antigen multiplier and moving time windows have the same effect on the DCA for this particular data set. The real-valued NSA with constant-sized detectors is not applicable to the data set, and the C4.5 decision tree algorithm provides a benchmark of the classification performance for this data set.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. U. Aickelin, P. Bentley, S. Cayzer, J. Kim, and J. McLeod. Danger Theory: The Link between AIS and IDS. Proceedings of the 2nd International Conference on Arti cial Immune Systems (ICARIS), LNCS2787:147{155, 2003.
    • 2. E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo. A geometric framework for unsupervised anomaly detection: Detecing intrusions in unlabled data. In D. Barbara and S. Jajodia, editors, Applications of Data Mining in Computer Security, chapter 4. Kluwer, 2002.
    • 3. A. Gonzalez and D. Dasgupta. Anomaly Detection Using Real-Valued Negative Selection. Genetic Programming and Evolvable Machines, 4(4):383{403, 2004.
    • 4. J. Greensmith and U. Aickelin. DCA for SYN Scan Detection. In Genetic and Evolutionary Computation Conference (GECCO), pages 49{56, 2007.
    • 5. J. Greensmith, U. Aickelin, and S. Cayzer. Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection. Proceedings of the 4th International Conference on Arti cial Immune Systems (ICARIS), LNCS3627:153{167, 2005.
    • 6. J. Greensmith, J. Twycross, and U. Aickelin. Articulation and Clari cation of the Dendritic Cell Algorithm. Proceedings of the 5th International Conference on Arti cial Immune Systems (ICARIS), LNCS4163:404{417, 2006.
    • 7. S. Hettich and S. D. Bay. The UCI KDD Archive [http://kdd.ics.uci.edu]. Technical report, Irvine, CA: University of California, Department of Information and Computer Science., 1999.
    • 8. MIT Lincoln Lab Information System Technology Group. The 1998 Intrusion Detection O -line Evaluation Plan. http://www.ll.mit.edu/IST/ideval/data/1998/, March 1998.
    • 9. Z. Ji and D. Dasgupta. Applicability Issues of the Real-Valued Negative Selecion Algorithms. In Genetic and Evolutionary Computation Conference (GECCO), pages 111 { 118, 2006.
    • 10. N. Kayacik, G. amd Zincir-Heywood and M. Heywood. On the Capability of an SOM based Intrusion Detection System. Proceedings of International Joint Conference on Neural Networks, 3:1808{ 1813, 2003.
    • 11. N. Kayacik, G. amd Zincir-Heywood and M. Heywood. Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets. In Third Annual Conference on Privacy, Security and Trust (PST), 2005.
    • 12. I. Levin. KDD-99 Classi er Learning Contest: LLSoft's Results Overview. SIGKDD Explorations, 1(2):67{75, 2000.
    • 13. T. M. Mitchell. Machine Learning. McGraw-Hill Series in Computer Science. McGraw-Hill, 1997.
    • 14. I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2nd edition, 2005.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

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