Combining Naive-Bayesian classifier and genetic clustering for effective anomaly based intrusion detection

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

Volumn 5908 LNAI, Issue , 2009, Pages 455-462

  Thamaraiselvi, S ^a   Srivathsan, R ^a   Imayavendhan, J ^a   Muthuregunathan, Raghavan ^a   Siddharth, S ^a  

^a ANNA UNIVERSITY   (India)

Author keywords

Anomaly; Feature selection; Genetic Algorithm; Genetic clustering; Intrusion detection; Na ve Bayesian classifier; NIDS

Indexed keywords

ANOMALY; ANOMALY-BASED INTRUSION DETECTION; BAYESIAN CLASSIFIER; CLASSIFICATION AND CLUSTERING; CLUSTERING TECHNIQUES; CONSISTENT PERFORMANCE; DATA MINING TECHNIQUES; DATA SETS; FEATURE SELECTION; GENETIC CLUSTERING ALGORITHMS; INTERNET BASED; INTRUSION DETECTION SYSTEMS; NETWORK INTRUSION DETECTION SYSTEMS; OPTIMAL SETS; SECURITY THREATS; TRAINING DATA SETS; TRAINING SETS;

BAYESIAN NETWORKS; CLASSIFIERS; CLUSTERING ALGORITHMS; COMPUTER CRIME; DATA MINING; FUZZY SETS; GRANULAR COMPUTING; ROUGH SET THEORY;

INTRUSION DETECTION;

EID: 76649099221     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-10646-0_55     Document Type: Conference Paper

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