MODEL FOR PHISHING WEBSITES CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK
DOI:
https://doi.org/10.15282/ijsecs.7.2.2021.1.0084Keywords:
Artificial Neural Network, classification, phishing websitesAbstract
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 model a suitable neural network classifier and then use the model to class the phishing website data set and evaluate the performance of the classifier. This research will use a phishing website data set which was retrieved from UCI repository and will be experimented using Encog Workbench tool. The main expected outcome from this study is the preliminary ANN classifier which classifies the target class of the phishing websites data set accurately, either phishy, suspicious or legitimate ones. The results indicate that ANN (9-5-1) model outperforms other models by achieving the highest accuracy and the least MSE value which is 0.04745.
References
B. Makhija, “‘REVIEW ON CLASSIFICATION TECHNIQUES IN DATA MINING,’” Int. J. Res. Comput. Inf. Technol.,
vol. 1, 2016.
V. Paramasivam, T. Sing, S. K. Dhillon, and A. S. Sidhu, “ScienceDirect A methodological review of data mining techniques
in predictive medicine : An application in hemodynamic prediction for abdominal aortic aneurysm disease,” Integr. Med. Res.,
vol. 34, no. 3, pp. 139–145, 2014.
V. Kunwar, K. Chandel, A. S. Sabitha, and A. Bansal, “Chronic Kidney Disease Analysis Using Data Mining Classification,”
Cloud Syst. Big Data Eng. (Confluence), 2016 6th Int. Conf. IEEE, pp. 300–305, 2016.
N. Satyanarayana, C. H. Ramalingaswamy, and Y. Ramadevi, “Survey of Classification Techniques in Data Mining,” 2014.
K. L. Taylor, Oracle Data Mining Concepts. 2010.
N. Jothi, N. Aini, A. Rashid, and W. Husain, “Data Mining in Healthcare – A Review,” Procedia - Procedia Comput. Sci.,
vol. 72, pp. 306–313, 2015.
V. Krishnaiah, G. Narsimha, and N. S. Chandra, “Diagnosis of Lung Cancer Prediction System Using Data Mining
Classification Techniques,” Int. J. Comput. Sci. Inf. Technol., vol. 4, no. 1, pp. 39–45, 2013.
MissingLink.ai, “Classification with Neural Networks: Is it the Right Choice? - MissingLink.ai,” 2016. [Online]. Available:
[Accessed: 18-Nov-2020].
K. Balasaravanan and M. Prakash, “Detection of dengue disease using artificial neural network based classification
technique,” vol. 7, pp. 13–15, 2018.
Anti-Phishing Working Group, “Phishing Activity Trends Report 3rd Quarter 2020,” Apwg, no. November, pp. 1–12, 2020.
D. Meharchandani, “Staggering Phishing Statistics in 2020 - Security Boulevard,” 2020. [Online]. Available:
https://securityboulevard.com/2020/12/staggering-phishing-statistics-in-2020/. [Accessed: 22-Jan-2021].
A. Kalybayev, “COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN WEBSITE PHISHING
DETECTION,” 2013.
N. Abdelhamid, A. Ayesh, and F. Thabtah, “Phishing detection based Associative Classification data mining,” vol. 41, no.
, pp. 5948–5959, 2014.
W. Hadi, F. Aburub, and S. Alhawari, “A new fast associative classification algorithm for detecting phishing websites,” Appl.
Soft Comput. J., vol. 48, pp. 729–734, 2016.
D. Zhang, Z. Yan, H. Jiang, and T. Kim, “A domain-feature enhanced classification model for the detection of Chinese
phishing e-Business websites,” Inf. Manag., vol. 51, no. 7, pp. 845–853, 2014.
M. Moghimi and A. Y. Varjani, “New rule-based phishing detection method,” Expert Syst. Appl., vol. 53, pp. 231–242, 2016.
F. Abdeljaber, R. M. Mohammad, F. Thabtah, and L. McCluskey, “Predicting Phishing Websites based on Self-Structuring
Neural Network,” 2014.
J. Heaton, “Encog Machine Learning Framework | Heaton Research.” [Online]. Available:
https://www.heatonresearch.com/encog/. [Accessed: 21-Jan-2021].
J. Heaton, Programming Neural Networks with Encog3 in Java. 2011.
A. C. del Castillo, Digital Twin Systems Modelling to Improve Real Time Assets Operation and Maintenance. 2. 2018.
S. Saxena, “Artificial Neuron Networks(Basics) | Introduction to Neural Networks | by Shubh Saxena | Becoming Human:
Artificial Intelligence Magazine,” 2017. [Online]. Available: https://becominghuman.ai/artificial-neuron-networks-basicsintroduction-to-neural-networks-3082f1dcca8c. [Accessed: 20-Nov-2020].
J. McCaffrey, “Neural Network Train-Validate-Test Stopping -- Visual Studio Magazine,” 13-May-2015. [Online]. Available:
https://visualstudiomagazine.com/Articles/2015/05/01/Train-Validate-Test-Stopping.aspx?Page=1. [Accessed: 17-Nov-
.
A. S. Fakharudin, N. Sulaiman, and N. Mustapha, “Artificial Neural Network Modelling of Biogas Production Processes,”
K. Chun, S. King, P. Chiong, and K. Ho, “Modeling electrostatic separation process using artificial neural network ( ANN ),”
Procedia - Procedia Comput. Sci., vol. 91, pp. 372–381, 2016.
Downloads
Published
Issue
Section
License
Copyright (c) 2021 Noor Hazirah Hassan, Abdul Sahli Fakharudin
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
