CANN: An intrusion detection system based on combining cluster centers and nearest neighbors

Knowledge-Based Systems

Volumn 78, Issue 1, 2015, Pages 13-21

  Lin, Wei Chao ^a   Ke, Shih Wen ^b   Tsai, Chih Fong ^c  

^a HWA HSIA INSTITUTE OF TECHNOLOGY   (Taiwan)

^b CHUNG YUAN CHRISTIAN UNIVERSITY   (Taiwan)

^c NATIONAL CENTRAL UNIVERSITY   (Taiwan)

Author keywords

Anomaly detection; Cluster center; Feature representation; Intrusion detection; Nearest neighbor

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL EFFICIENCY; COMPUTER CRIME; COMPUTER NETWORKS; COMPUTER SYSTEM FIREWALLS; FEATURE EXTRACTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; ONE DIMENSIONAL; SECURITY OF DATA;

ANOMALY DETECTION; CLASSIFICATION ACCURACY; CLUSTER CENTERS; DETECTION PERFORMANCE; DISTANCE-BASED FEATURES; FEATURE REPRESENTATION; INTRUSION DETECTION SYSTEMS; NEAREST NEIGHBORS;

INTRUSION DETECTION;

EID: 84933183260     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2015.01.009     Document Type: Article

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