Application of growing hierarchical SOM for visualisation of network forensics traffic data

Neural Networks

Volumn 32, Issue , 2012, Pages 275-284

  Palomo, E J ^a   North, J ^b   Elizondo, D ^b   Luque, R M ^a   Watson, T ^b  

^a UNIVERSITY OF MÁLAGA   (Spain)

^b DE MONTFORT UNIVERSITY   (United Kingdom)

Author keywords

Data clustering; Data visualisation; Feature extraction; Hierarchical self organisation; Network forensics

Indexed keywords

DATA CLUSTERING; DATA MINING APPLICATIONS; DIGITAL CRIME; DIGITAL EVIDENCE; DIGITAL INVESTIGATION; HIERARCHICAL ARCHITECTURES; HIGH VARIABILITY; INPUT DATAS; NETWORK ENVIRONMENTS; NETWORK FORENSICS; NETWORK TRAFFIC; QUALITATIVE FEATURES; QUANTITATIVE FEATURES; SELF-ORGANISATION; SELF-ORGANISING MAPS; TRAFFIC DATA;

CLUSTERING ALGORITHMS; CRIME; DATA VISUALIZATION; FEATURE EXTRACTION; INPUT OUTPUT PROGRAMS; VISUALIZATION;

COMPUTER CRIME;

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; CLASSIFICATION ALGORITHM; COMPUTER INPUT DEVICE; COMPUTER NETWORK; COMPUTER PROGRAM; DATA ANALYSIS SOFTWARE; FORENSIC MEDICINE; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; SELF ORGANISING MAP; SIGNAL NOISE RATIO;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTER COMMUNICATION NETWORKS; CRIME; FORENSIC SCIENCES; INTERNET; NEURAL NETWORKS (COMPUTER); NEURONS; NONLINEAR DYNAMICS; SOFTWARE;

EID: 84861766306     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2012.02.021     Document Type: Article

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