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2013 IEEE 13th International Conference on Data Mining Workshops (2013)
Dec. 7, 2013 to Dec. 10, 2013
pp: 521-528
The advantage of graph-based anomaly detection is that the relationships between elements can be analyzed for structural oddities that could represent activities such as fraud, network intrusions, or suspicious associations in a social network. However, current approaches to detecting anomalies in graphs are computationally expensive and do not scale to large graphs. For instance, in the case of computer network traffic, a graph representation of the traffic might consist of nodes representing computers and edges representing communications between the corresponding computers. However, computer network traffic is typically voluminous, or acquired in real-time as a stream of information. In this work, we describe methods for graph-based anomaly detection via graph partitioning and windowing, and demonstrate their ability to efficiently detect anomalies in data represented as a graph.
Image edge detection, Telecommunication traffic, Internet, Computers, Scalability, Buildings

W. Eberle and L. Holder, "Incremental Anomaly Detection in Graphs," 2013 IEEE 13th International Conference on Data Mining Workshops(ICDMW), TX, USA USA, 2013, pp. 521-528.
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