Phishing website detection fuzzy system modelling

Phoebe Barraclough; Graham Sexton

doi:10.1109/SAI.2015.7237323

Phishing website detection fuzzy system modelling

Phoebe Barraclough, Graham Sexton

School of Computer Science

Research output: Contribution to conference › Paper

15 Citations (Scopus)

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.

Original language	English
DOIs	https://doi.org/10.1109/SAI.2015.7237323
Publication status	Published - Jul 2015
Event	SAI 2015 - Science and Information Conference - London Duration: 1 Jul 2015 → …

Conference

Conference	SAI 2015 - Science and Information Conference
Period	1/07/15 → …

Keywords

fuzzy system
parameters
phishing detection

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10.1109/SAI.2015.7237323

Cite this

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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.",

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AB - 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.

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KW - parameters

KW - phishing detection

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M3 - Paper

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Phishing website detection fuzzy system modelling

Abstract

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Keywords

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