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ICDM
2005
IEEE
116views Data Mining» more  ICDM 2005»
15 years 3 months ago
Learning Functional Dependency Networks Based on Genetic Programming
Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle dis...
Wing-Ho Shum, Kwong-Sak Leung, Man Leung Wong
ISMIS
2005
Springer
15 years 3 months ago
Learning the Daily Model of Network Traffic
Abstract. Anomaly detection is based on profiles that represent normal behaviour of users, hosts or networks and detects attacks as significant deviations from these profiles. In t...
Costantina Caruso, Donato Malerba, Davide Papagni
ICNC
2005
Springer
15 years 3 months ago
Applying Genetic Programming to Evolve Learned Rules for Network Anomaly Detection
The DARPA/MIT Lincoln Laboratory off-line intrusion detection evaluation data set is the most widely used public benchmark for testing intrusion detection systems. But the presence...
Chuanhuan Yin, Shengfeng Tian, Houkuan Huang, Jun ...
ICPR
2008
IEEE
15 years 10 months ago
Collaborative learning by boosting in distributed environments
In this paper we propose a new distributed learning method called distributed network boosting (DNB) algorithm for distributed applications. The learned hypotheses are exchanged b...
Shijun Wang, Changshui Zhang
ICANN
2003
Springer
15 years 2 months ago
The Acquisition of New Categories through Grounded Symbols: An Extended Connectionist Model
Abstract. Solutions to the symbol grounding problem, in psychologically plausible cognitive models, have been based on hybrid connectionist/symbolic architectures, on robotic appro...
Alberto Greco, Thomas Riga, Angelo Cangelosi