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» Detecting Anomalies in Graphs
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NIPS
2004
13 years 8 months ago
Density Level Detection is Classification
We show that anomaly detection can be interpreted as a binary classification problem. Using this interpretation we propose a support vector machine (SVM) for anomaly detection. We...
Ingo Steinwart, Don R. Hush, Clint Scovel
SDM
2008
SIAM
206views Data Mining» more  SDM 2008»
13 years 8 months ago
Latent Variable Mining with Its Applications to Anomalous Behavior Detection
In this paper, we propose a new approach to anomaly detection by looking at the latent variable space to make the first step toward latent anomaly detection. Most conventional app...
Shunsuke Hirose, Kenji Yamanishi
TON
2008
106views more  TON 2008»
13 years 7 months ago
Statistical techniques for detecting traffic anomalies through packet header data
This paper proposes a traffic anomaly detector, operated in postmortem and in real-time, by passively monitoring packet headers of traffic. The frequent attacks on network infrastr...
Seong Soo Kim, A. L. Narasimha Reddy
PKDD
2010
Springer
141views Data Mining» more  PKDD 2010»
13 years 5 months ago
On Detecting Clustered Anomalies Using SCiForest
Detecting local clustered anomalies is an intricate problem for many existing anomaly detection methods. Distance-based and density-based methods are inherently restricted by their...
Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou
ICDM
2009
IEEE
150views Data Mining» more  ICDM 2009»
13 years 5 months ago
Filtering and Refinement: A Two-Stage Approach for Efficient and Effective Anomaly Detection
Anomaly detection is an important data mining task. Most existing methods treat anomalies as inconsistencies and spend the majority amount of time on modeling normal instances. A r...
Xiao Yu, Lu An Tang, Jiawei Han