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CONEXT
2007
ACM
13 years 10 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
SDM
2004
SIAM
211views Data Mining» more  SDM 2004»
13 years 10 months ago
Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects
Databases are a key technology for molecular biology which is a very data intensive discipline. Since molecular biological databases are rather heterogeneous, unification and data...
Hans-Peter Kriegel, Peer Kröger, Alexey Pryak...
ICPR
2004
IEEE
14 years 10 months ago
Face Detection Using Discriminating Feature Analysis and Support Vector Machine in Video
This paper presents a novel face detection method in video by using Discriminating Feature Analysis (DFA) and Support Vector Machine (SVM). Our method first incorporates temporal ...
Chengjun Liu, Peichung Shih
CCS
2009
ACM
14 years 3 months ago
Active learning for network intrusion detection
Anomaly detection for network intrusion detection is usually considered an unsupervised task. Prominent techniques, such as one-class support vector machines, learn a hypersphere ...
Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf...
JSS
2002
198views more  JSS 2002»
13 years 8 months ago
Automated discovery of concise predictive rules for intrusion detection
This paper details an essential component of a multi-agent distributed knowledge network system for intrusion detection. We describe a distributed intrusion detection architecture...
Guy G. Helmer, Johnny S. Wong, Vasant Honavar, Les...