A hybrid anomaly detection framework in cloud computing using one-class and two-class support vector machines

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

Volumn 7713 LNAI, Issue , 2012, Pages 726-738

  Fu, Song ^a   Liu, Jianguo ^a   Pannu, Husanbir ^a  

^a UNIVERSITY OF NORTH TEXAS   (United States)

Author keywords

Anomaly detection; Classification; Cloud computing; Clustering; Dependable computing

Indexed keywords

ANOMALY DETECTION; CLUSTERING; COMPUTING SYSTEM; DEPENDABILITY ASSURANCE; DEPENDABLE COMPUTING; DETECTION ACCURACY; DETECTION SENSITIVITY; RESOURCE MANAGEMENT; SELF-MANAGING; STORAGE SERVERS; SYSTEM COMPLEXITY; SYSTEM LEVELS; TWO-CLASS SUPPORT VECTOR MACHINES;

CLASSIFICATION (OF INFORMATION); CLOUD COMPUTING; SUPPORT VECTOR MACHINES;

COMPUTER SYSTEMS;

EID: 84872710120     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-35527-1_60     Document Type: Conference Paper

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