Feature weighting and selection for a real-time network intrusion detection system based on GA with KNN

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

Volumn 5075 LNCS, Issue , 2008, Pages 195-204

  Su, Ming Yang ^a   Chang, Kai Chi ^a   Wei, Hua Fu ^a   Lin, Chun Yuen ^a  

^a MING CHUAN UNIVERSITY   (Taiwan)

Author keywords

DoS DDoS Attacks; Genetic Algorithm; KNN (k Nearest Neighbor); Network Security; NIDS (Network Intrusion Detection System)

Indexed keywords

BIOELECTRIC PHENOMENA; COMPUTER CRIME; DIESEL ENGINES; GALLIUM; GENETIC ALGORITHMS; INTERNET; INTERSYMBOL INTERFERENCE; SECURITY OF DATA; SENSORS; FEATURE EXTRACTION; LEARNING ALGORITHMS; MOTION COMPENSATION; NETWORK SECURITY;

(R ,S ,S) POLICY; ACCURACY RATE; APPLIED (CO); DOS/DDOS ATTACKS; FEATURE SELECTION (FS); FEATURE WEIGHTING; HEIDELBERG (CO); INTERNATIONAL CONFERENCES; INTERNATIONAL WORKSHOPS; K NEAREST NEIGHBOR (K-NN); NETWORK INTRUSION DETECTION SYSTEM (NIDS); SECURITY INFORMATICS; SPRINGER (CO); TRAINING PHASE;

INTRUSION DETECTION;

DOS/DDOS ATTACKS; FEATURE WEIGHTING; K-NEAREST NEIGHBORS; NETWORK INTRUSION DETECTION SYSTEMS; OVERALL ACCURACIES; REAL-TIME NETWORKS; TRAINING PHASE; UNKNOWN ATTACKS;

EID: 45849149267     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-69304-8_20     Document Type: Conference Paper

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