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Investigations of Intrusion Detection Based on Data Mining

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 124))

Abstract

This thesis compares the availability of clustering algorithm and Apriori algorithm in the intrusion detection system. Experiments prove that the clustering algorithm bears better results on Probing and DOS than Apriori algorithm. When detecting intrusion, both algorithms suffer a high rate of missing report, especially when detecting U2R and R2L.

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References

  1. Kumar, S., Spafford, E.: An Application of Pattern Matching in Intrusion Detection, Department of computer Science Purdue University, CSD-TR-94-103, Coast TR 94-07 (1994)

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© 2012 Springer-Verlag Berlin Heidelberg

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Wang, M., Zhao, A. (2012). Investigations of Intrusion Detection Based on Data Mining. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25781-0_41

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  • DOI: https://doi.org/10.1007/978-3-642-25781-0_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25780-3

  • Online ISBN: 978-3-642-25781-0

  • eBook Packages: EngineeringEngineering (R0)

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