DocumentCode :
2035043
Title :
Phishing website detection fuzzy system modelling
Author :
Barraclough, Phoebe ; Sexton, Graham
Author_Institution :
Comput. Sci. & Digital Technol., Northumbria Univ., Newcastle upon Tyne, UK
fYear :
2015
fDate :
28-30 July 2015
Firstpage :
1384
Lastpage :
1386
Abstract :
This study investigates and identifies parameters in a single platform based on fuzzy system and neural network for phishing websites detection. The new approach utilizes Fuzzy systems, neural network with a set of parameters and a data set to detect phishing sites with high accuracy in real-time. A total of 300 data from six sources were used as training and testing sets using 2-fold cross-validation to train and validate the model, which has achieved the best performance (99.6%) compared to other results in the field.
Keywords :
Web sites; computer crime; fuzzy systems; neural nets; parameter estimation; unsolicited e-mail; 2-fold cross-validation; Website detection; data set; fuzzy system modelling; neural network; parameters identification; phishing sites; Accuracy; Feature extraction; Fuzzy logic; Fuzzy systems; Testing; Training; Tuning; fuzzy system; parameters; phishing detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Science and Information Conference (SAI), 2015
Conference_Location :
London
Type :
conf
DOI :
10.1109/SAI.2015.7237323
Filename :
7237323
Link To Document :
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