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» Anomaly detection in data represented as graphs
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ICDM
2008
IEEE
117views Data Mining» more  ICDM 2008»
14 years 1 months ago
RTM: Laws and a Recursive Generator for Weighted Time-Evolving Graphs
How do real, weighted graphs change over time? What patterns, if any, do they obey? Earlier studies focus on unweighted graphs, and, with few exceptions, they focus on static snap...
Leman Akoglu, Mary McGlohon, Christos Faloutsos
KDD
2009
ACM
193views Data Mining» more  KDD 2009»
14 years 2 months ago
Category detection using hierarchical mean shift
Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to iden...
Pavan Vatturi, Weng-Keen Wong
ICDM
2008
IEEE
126views Data Mining» more  ICDM 2008»
14 years 1 months ago
Detecting Suspicious Behavior in Surveillance Images
We introduce a novel technique to detect anomalies in images. The notion of normalcy is given by a baseline of images, under the assumption that the majority of such images is nor...
Daniel Barbará, Carlotta Domeniconi, Zoran ...
SP
2008
IEEE
176views Security Privacy» more  SP 2008»
14 years 1 months ago
Casting out Demons: Sanitizing Training Data for Anomaly Sensors
The efficacy of Anomaly Detection (AD) sensors depends heavily on the quality of the data used to train them. Artificial or contrived training data may not provide a realistic v...
Gabriela F. Cretu, Angelos Stavrou, Michael E. Loc...
IEEEARES
2006
IEEE
14 years 1 months ago
Identifying Intrusions in Computer Networks with Principal Component Analysis
Most current anomaly Intrusion Detection Systems (IDSs) detect computer network behavior as normal or abnormal but cannot identify the type of attacks. Moreover, most current intr...
Wei Wang, Roberto Battiti