PHISHING WEBSITES CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK


PHISHING WEBSITES CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK

N.H. Hassan1,*, A.S. Fakharudin1
1Faculty of Computing, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia.

ABSTRACT
Internet users might be exposed to various forms of threats that can create economic harm, identity fraud, and lack of faith in e-commerce and online banking by consumers as the internet has become a necessary part of everyday activities. Phishing can be regarded as a type of web extortions described as the skill of imitating an honest company's website aimed at obtaining private information for example usernames, passwords, and bank information. The accuracy of classification is very significant in order to produce high accuracy results and least error rate in classification of phishing websites. The objective of this research is to design a suitable neural network model, implement it in a phishing website data set and evaluate the performance of the neural network using an error indicator, known as mean squared error (MSE) from the results of the experiment. This research will implement a classification technique called Artificial Neural Network (ANN) which is a well-known technique for classification of data. The main aim of ANN employed in this study is to classify phishing websites into a correct class. This research will use a phishing website data set which retrieved from UCI repository and will be experimented using Encog Workbench tool. The main expected outcome from this study is that the ANN algorithm can classify the target class of the phishing websites data set accurately either phishy, suspicious or legitimate ones and produce least MSE value.

Keywords: Artificial Neural Network, classification, phishing websites

pdf ico FULL PAPER

 
 
 
 
 

Contact Us

Managing Editor of IJSECS
Faculty of Computing,
College of Computing and Applied Sciences

Universiti Malaysia Pahang
Lebuhraya Tun Razak
26300 Gambang,
Kuantan, Pahang Darul Makmur.

Tel: +609 549 2133
Fax: +609 549 2144
Email: ijsecsfskkp@ump.edu.my

Visitor Counter

0141881
Today
Yesterday
This Week
Last Week
This Month
Last Month
All days
114
87
176
734
2466
3169
141881