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CONEXT
2007
ACM
14 years 11 months ago
Detecting worm variants using machine learning
Network intrusion detection systems typically detect worms by examining packet or flow logs for known signatures. Not only does this approach mean worms cannot be detected until ...
Oliver Sharma, Mark Girolami, Joseph S. Sventek
CORR
2010
Springer
110views Education» more  CORR 2010»
14 years 10 months ago
Real-Time Alert Correlation with Type Graphs
The premise of automated alert correlation is to accept that false alerts from a low level intrusion detection system are inevitable and use attack models to explain the output in ...
Gianni Tedesco, Uwe Aickelin
ICC
2007
IEEE
164views Communications» more  ICC 2007»
15 years 4 months ago
A Framework of Attacker Centric Cyber Attack Behavior Analysis
—Cyber attack behavior analysis can be roughly classified as “network centric” and “attacker centric” approaches. Compared with traditional “network centric” approach...
Xuena Peng, Hong Zhao
ICIP
2002
IEEE
15 years 11 months ago
Hybrid and parallel face classifier based on artificial neural networks and principal component analysis
We present a hybrid and parallel system based on artificial neural networks for a face invariant classifier and general pattern recognition problems. A set of face features is ext...
Peter V. Bazanov, Tae-Kyun Kim, Seok-Cheol Kee, Sa...
ICIP
2003
IEEE
15 years 3 months ago
Image classification using multimedia knowledge networks
This paper presents novel methods for classifying images based on knowledge discovered from annotated images using WordNet. The novelty of this work is the automatic class discove...
Ana B. Benitez, Shih-Fu Chang