Abstract
The attribute oriented induction (AOI) is a kind of aggregation method. By generalizing the attributes of the alert, it creates several clusters that includes a set of alerts having similar or the same cause. However, if the attributes are excessively abstracted, the administrator does not identify the root cause of the attack. In addition, deciding time interval of clustering and deciding min_size are one of the most critical problems. In this paper, we describe about the over-generalization problem because of the unbalanced generalization hierarchy and discuss the solution of the problem. We also discuss problem to decide time interval and meaningful min_size, and propose reasonable method to solve these problems.
This study is supported by the National Security Research Institute in Korea and the Brain Korea 21 project in 2004.
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Kim, J., Lee, G., Seo, Jt., Park, Ek., Park, Cs., Kim, Dk. (2005). Y-AOI: Y-Means Based Attribute Oriented Induction Identifying Root Cause for IDSs. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_25
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DOI: https://doi.org/10.1007/11540007_25
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