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When a Family of Iris Flower is Normal, Then are Others Abnormal?

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Abstract

This article is not a report of success but rather a challenge to those who claim to have successfully designed a network intrusion detection system by means of a machine learning technique using arti.cial dataset to train and to test the system.

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References

  1. A. Imada (2006) “How Many Parachutists will be Needed to Find a Needle in a Pastoral? – Who is a lucky one?” Proceedings of the International Conference on Neural Networks and Artificial Intelligence, pp. 53–60.

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  2. G. Castellano, and A. M. Fanelli(2000) “Fuzzy Inference and Rule Extraction using a Neural Network.” Neural Network World Journal Vol. 3, pp. 361–371.

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  3. M. Sabhnani, and G. Serpen (2003) “Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context.” Proceedings of the International Conference on Machine Learning: Models, Technologies and Applications, pp. 209–215.

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  4. M. Ayara, J. Timmis, R. D. Lemos, L. N. D. Castro, and R. Duncan (2002) “Negative Selection: How to Generate Detectors.” Proceedings of 1st International Conference on Artificial Immune Systems, pp. 89–98.

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  5. J. Gomez, F. Gonzalez, and D. Dasgupta (2003) “An Immuno-Fuzzy Approach to Anomaly Detection.” Proceedings of IEEE International Conference on Fuzzy Systems, Vol. 2, pp. 1219–1224.

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© 2007 Springer Science+Business Media, LLC

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Imada, A. (2007). When a Family of Iris Flower is Normal, Then are Others Abnormal?. In: Pejaś, J., Saeed, K. (eds) Advances in Information Processing and Protection. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73137-7_18

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  • DOI: https://doi.org/10.1007/978-0-387-73137-7_18

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-73136-0

  • Online ISBN: 978-0-387-73137-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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