An effective intrusion detection model based on random forest and neural networks

Advanced Materials Research

Volumn 267, Issue , 2011, Pages 308-313

  Zhong, Shaohong ^a   Huang, Huajun ^a   Chen, Aibin ^a  

^a CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY   (China)

Author keywords

Intrusion detection; Neural networks; Random forest

Indexed keywords

COMPUTATIONALLY EFFICIENT; DATA FEATURE; DATA MINING METHODS; DATA SETS; HYBRID MODEL; INTRUSION DETECTION MODELS; INTRUSION DETECTION SYSTEMS; NETWORK INTRUSION DETECTION SYSTEMS; RANDOM FOREST; RANDOM FOREST METHODS; RANDOM FORESTS; RESEARCH DOMAINS;

COMPUTER CRIME; DECISION TREES; FEATURE EXTRACTION; INDUSTRIAL APPLICATIONS; INDUSTRIAL RESEARCH; MANUFACTURE; NETWORK SECURITY; NEURAL NETWORKS; RESEARCH LABORATORIES;

INTRUSION DETECTION;

EID: 79960464355     PISSN: 10226680     EISSN: None     Source Type: Book Series    
DOI: 10.4028/www.scientific.net/AMR.267.308     Document Type: Conference Paper

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