Conference proceeding
Graph-based anomaly detection
Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, pp.631-636
KDD '03
08/24/2003
Handle:
https://hdl.handle.net/2376/115614
Abstract
Anomaly detection is an area that has received much attention in recent years. It has a wide variety of applications, including fraud detection and network intrusion detection. A good deal of research has been performed in this area, often using strings or attribute-value data as the medium from which anomalies are to be extracted. Little work, however, has focused on anomaly detection in graph-based data. In this paper, we introduce two techniques for graph-based anomaly detection. In addition, we introduce a new method for calculating the regularity of a graph, with applications to anomaly detection. We hypothesize that these methods will prove useful both for finding anomalies, and for determining the likelihood of successful anomaly detection within graph-based data. We provide experimental results using both real-world network intrusion data and artificially-created data.
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Details
- Title
- Graph-based anomaly detection
- Creators
- Caleb NobleDiane Cook
- Publication Details
- Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, pp.631-636
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Series
- KDD '03
- Publisher
- ACM
- Identifiers
- 99900548146501842
- Language
- English
- Resource Type
- Conference proceeding