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» Active learning for network intrusion detection
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JMLR
2010
185views more  JMLR 2010»
13 years 5 months ago
HMMPayl: an application of HMM to the analysis of the HTTP Payload
Zero-days attacks are one of the most dangerous threats against computer networks. These, by definition, are attacks never seen before. Thus, defense tools based on a database of ...
Davide Ariu, Giorgio Giacinto
CN
2010
183views more  CN 2010»
13 years 11 months ago
A learning automata based scheduling solution to the dynamic point coverage problem in wireless sensor networks
The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as little sensor nodes as possible. One way ...
Mehdi Esnaashari, Mohammad Reza Meybodi
KESAMSTA
2009
Springer
14 years 3 months ago
Structural Changes in an Email-Based Social Network
ent Artificial Immune Systems (MAAIS) for Intrusion Detection: Abstraction from Danger Theory Chung-Ming Ou, C.R. Ou Ants-like Agents: a Model and Analysis Based on Natural Ants Be...
Krzysztof Juszczyszyn, Katarzyna Musial
KDD
2003
ACM
148views Data Mining» more  KDD 2003»
14 years 11 months ago
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
JMLR
2008
148views more  JMLR 2008»
13 years 10 months ago
Linear-Time Computation of Similarity Measures for Sequential Data
Efficient and expressive comparison of sequences is an essential procedure for learning with sequential data. In this article we propose a generic framework for computation of sim...
Konrad Rieck, Pavel Laskov