Title:
A Feature-based Sampling Method to Detect Anomalous Patterns in High Dimensional Datasets
A Feature-based Sampling Method to Detect Anomalous Patterns in High Dimensional Datasets
Author(s)
Nguyen, Minh Quoc
Mark, Leo
Omiecinski, Edward
Mark, Leo
Omiecinski, Edward
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Abstract
We introduce a feature-based sampling method to detect
anomalous patterns. By recognizing that an observation
is considered normal because there are many observations
similar to it, we formally define the problem of anomalous
pattern detection. The properties of normal and anomalous
patterns allow us to devise a generic framework using
the sampling method to quickly prune the normal observations.
Observations that can not form significant patterns
are anomalous. Rules that are learned from the dataset are
used to construct the patterns for which we compute a score
function to measure the interestingness of the anomalous
patterns. Experiments using the KDD Cup 99 dataset show
that our approach can discover most of the attack patterns.
Those attacks are in the top set of anomalous patterns and
have a higher score than the patterns of normal connections.
The experiments also show that the algorithm can run in
near linear time.
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Date Issued
2008
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Resource Type
Text
Resource Subtype
Technical Report