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» Anomaly Detection in P2P Networks Using Markov Modelling
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SP
2008
IEEE
176views Security Privacy» more  SP 2008»
14 years 3 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...
ICPR
2002
IEEE
14 years 9 months ago
Relational Graph Labelling Using Learning Techniques and Markov Random Fields
This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road networ...
Denis Rivière, Jean-Francois Mangin, Jean-M...
JAIR
2010
181views more  JAIR 2010»
13 years 3 months ago
Intrusion Detection using Continuous Time Bayesian Networks
Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor sys...
Jing Xu, Christian R. Shelton
SP
1999
IEEE
184views Security Privacy» more  SP 1999»
14 years 26 days ago
A Data Mining Framework for Building Intrusion Detection Models
There is often the need to update an installed Intrusion Detection System (IDS) due to new attack methods or upgraded computing environments. Since many current IDSs are construct...
Wenke Lee, Salvatore J. Stolfo, Kui W. Mok
CORR
2010
Springer
147views Education» more  CORR 2010»
13 years 8 months ago
High-Rate Quantization for the Neyman-Pearson Detection of Hidden Markov Processes
This paper investigates the decentralized detection of Hidden Markov Processes using the NeymanPearson test. We consider a network formed by a large number of distributed sensors....
Joffrey Villard, Pascal Bianchi, Eric Moulines, Pa...