NDLI: Anomaly pattern detection in categorical datasets

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Anomaly pattern detection in categorical datasets

Content Provider	ACM Digital Library
Author	Neill, Daniel B. Schneider, Jeff Das, Kaustav
Abstract	We propose a new method for detecting patterns of anomalies in categorical datasets. We assume that anomalies are generated by some underlying process which affects only a particular subset of the data. Our method consists of two steps: we first use a "local anomaly detector" to identify individual records with anomalous attribute values, and then detect patterns where the number of anomalous records is higher than expected. Given the set of anomalies flagged by the local anomaly detector, we search over all subsets of the data defined by any set of fixed values of a subset of the attributes, in order to detect self-similar patterns of anomalies. We wish to detect any such subset of the test data which displays a significant increase in anomalous activity as compared to the normal behavior of the system (as indicated by the training data). We perform significance testing to determine if the number of anomalies in any subset of the test data is significantly higher than expected, and propose an efficient algorithm to perform this test over all such subsets of the data. We show that this algorithm is able to accurately detect anomalous patterns in real-world hospital, container shipping and network intrusion data.
Starting Page	169
Ending Page	176
Page Count	8
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1401915
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Machine learning Pattern detection Anomaly detection
Content Type	Text
Resource Type	Article

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