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» Active learning for network intrusion detection
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IDEAL
2004
Springer
14 years 4 months ago
Detecting Worm Propagation Using Traffic Concentration Analysis and Inductive Learning
As a vast number of services have been flooding into the Internet, it is more likely for the Internet resources to be exposed to various hacking activities such as Code Red and SQL...
Sanguk Noh, Cheolho Lee, Keywon Ryu, Kyunghee Choi...
ICON
2007
IEEE
14 years 5 months ago
Lightweight Detection of DoS Attacks
Denial of Service (DoS) attacks have continued to evolve and they impact the availability of Internet infrastructure. Many researchers in the field of network security and system ...
Sirikarn Pukkawanna, Vasaka Visoottiviseth, Panita...
AINA
2009
IEEE
14 years 5 months ago
Similarity Search over DNS Query Streams for Email Worm Detection
Email worms continue to be a persistent problem, indicating that current approaches against this class of selfpropagating malicious code yield rather meagre results. Additionally,...
Nikolaos Chatzis, Nevil Brownlee
ICCS
2007
Springer
14 years 4 months ago
DDDAS/ITR: A Data Mining and Exploration Middleware for Grid and Distributed Computing
We describe our project that marries data mining together with Grid computing. Specifically, we focus on one data mining application - the Minnesota Intrusion Detection System (MIN...
Jon B. Weissman, Vipin Kumar, Varun Chandola, Eric...
ICDM
2005
IEEE
187views Data Mining» more  ICDM 2005»
14 years 4 months ago
Parallel Algorithms for Distance-Based and Density-Based Outliers
An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism. Outlier detection has many applic...
Elio Lozano, Edgar Acuña