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» Detecting Anomalies in Graphs
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DSN
2005
IEEE
14 years 2 months ago
The Effects of Algorithmic Diversity on Anomaly Detector Performance
Common practice in anomaly-based intrusion detection assumes that one size fits all: a single anomaly detector should detect all anomalies. Compensation for any performance short...
Kymie M. C. Tan, Roy A. Maxion
KDD
2008
ACM
165views Data Mining» more  KDD 2008»
14 years 9 months ago
Colibri: fast mining of large static and dynamic graphs
Low-rank approximations of the adjacency matrix of a graph are essential in finding patterns (such as communities) and detecting anomalies. Additionally, it is desirable to track ...
Hanghang Tong, Spiros Papadimitriou, Jimeng Sun, P...
ICARIS
2007
Springer
14 years 2 months ago
Towards a Novel Immune Inspired Approach to Temporal Anomaly Detection
Abstract. In this conceptual paper, we report on studies and initial definitions of an immune-inspired approach to temporal anomaly detection problems, where there is a strict temp...
Thiago S. Guzella, Tomaz A. Mota-Santos, Walmir M....
IPPS
2006
IEEE
14 years 2 months ago
A nonself space approach to network anomaly detection
The paper presents an approach for the anomaly detection problem based on principles of immune systems. Flexibility and efficiency of the anomaly detection system are achieved by ...
Marek Ostaszewski, Franciszek Seredynski, Pascal B...
RAID
2004
Springer
14 years 1 months ago
Anomaly Detection Using Layered Networks Based on Eigen Co-occurrence Matrix
Anomaly detection is a promising approach to detecting intruders masquerading as valid users (called masqueraders). It creates a user profile and labels any behavior that deviates...
Mizuki Oka, Yoshihiro Oyama, Hirotake Abe, Kazuhik...