Approaches to Abnormality Detection with Constraints
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2006
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=osu1150484039 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-osu11504840392021-08-03T05:51:18Z Approaches to Abnormality Detection with Constraints Otey, Matthew Eric Computer Science abnormality detection anomaly detection signature detection outlier detection data mining A common problem in data analysis is that of discriminating between modes of normal behavior and modes of abnormal behavior. Of particular interest are techniques that can automatically detect abnormal activity in data. This is important since abnormal data may be indicative of measurement error in scientific data, or malicious activity in security audit data. There are two basic approaches to the problem of automatically finding abnormalities. The first is known as signature detection, which involves finding known patterns of abnormality in a database. However, it has the drawback of not being able to detect abnormalities for which there is no prior information. The second approach is known as anomaly detection, which involves building a model of normal data and then searching for patterns that do not fit this model. Unlike the signature detection approach, it is able to detect abnormalities for which there is no prior information, but has the drawback that the anomalies it does detect may not be (significantly) abnormal. The most successful approaches will use both signature detection and anomaly detection techniques to utilize their combined strengths. Much of the previous research in this area has focused on more general approaches to anomaly and signature detection. However, this work is focused on carrying out anomaly and signature detection under various constraints. For example, the data may contain heterogeneous attribute types, or have missing values. The data may also be distributed across several computers or streaming in at a high rate of speed, or there may be limitations on the resources available to analyze the data. In this work, we develop novel solutions to the abnormality detection problem with constraints, and empirically test them on various real and synthetic data sets. 2006-09-12 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1150484039 http://rave.ohiolink.edu/etdc/view?acc_num=osu1150484039 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Computer Science abnormality detection anomaly detection signature detection outlier detection data mining |
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Computer Science abnormality detection anomaly detection signature detection outlier detection data mining Otey, Matthew Eric Approaches to Abnormality Detection with Constraints |
| author |
Otey, Matthew Eric |
| author_facet |
Otey, Matthew Eric |
| author_sort |
Otey, Matthew Eric |
| title |
Approaches to Abnormality Detection with Constraints |
| title_short |
Approaches to Abnormality Detection with Constraints |
| title_full |
Approaches to Abnormality Detection with Constraints |
| title_fullStr |
Approaches to Abnormality Detection with Constraints |
| title_full_unstemmed |
Approaches to Abnormality Detection with Constraints |
| title_sort |
approaches to abnormality detection with constraints |
| publisher |
The Ohio State University / OhioLINK |
| publishDate |
2006 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1150484039 |
| work_keys_str_mv |
AT oteymattheweric approachestoabnormalitydetectionwithconstraints |
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