An incremental-learning method for supervised anomaly detection by cascading service classifier and ITI decision tree methods

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5477, Issue , 2009, Pages 155-160

  Yu, Wei Yi ^a   Lee, Hahn Ming ^a  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Anomaly detection system (ADS); ITI (incremental tree inducer); K Means clustering; KDD 99; Kohonen self organizing maps (SOM)

Indexed keywords

ANOMALY DETECTION; ANOMALY DETECTION SYSTEM (ADS); ASSIGNMENT PROBLEMS; COMBINATION RULES; DATA SETS; DECISION TREE METHOD; DETECTION RATES; DIFFERENT SERVICES; FALSE POSITIVE RATES; HANDLING MISSING VALUES; INCREMENTAL LEARNING; ITI (INCREMENTAL TREE INDUCER); K-MEANS; K-MEANS CLUSTERING; LEARNING METHODS; NEAREST NEIGHBORS; NEW SERVICES; SERVICE CLUSTERS;

CLASSIFIERS; COMPUTER NETWORKS; CONFORMAL MAPPING; DECISION TREES; EDUCATION; LEARNING SYSTEMS; SELF ORGANIZING MAPS; STRENGTH OF MATERIALS; WATER SUPPLY SYSTEMS;

DATA HANDLING;

EID: 67649946388     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01393-5_17     Document Type: Conference Paper

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