Classification of phishing websites using machine learning techniques

Phishing detection is a momentous problem which can be deliberated by many researchers with numerous advanced approaches. Current anti-phishing mechanisms such as blacklist-base anti-phishing, Heuristic-based anti-phishing does suffer low detection accuracy and high false alarm. There is need for ef...

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Main Author:	Zamani, Hadi
Format:	Thesis
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
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id	my-utm-ep.42243
record_format	uketd_dc
spelling	my-utm-ep.422432020-08-23T06:04:25Z Classification of phishing websites using machine learning techniques 2013 Zamani, Hadi TK Electrical engineering. Electronics Nuclear engineering Phishing detection is a momentous problem which can be deliberated by many researchers with numerous advanced approaches. Current anti-phishing mechanisms such as blacklist-base anti-phishing, Heuristic-based anti-phishing does suffer low detection accuracy and high false alarm. There is need for efficient mechanism to protect user from phishing websites. The purpose of this study is to investigate the capability of 6 machine learning algorithms which they are Multi- Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR) and Naïve Bayes (NB) to classify phishing and non-phishing websites. These algorithms were trained with two different groups of training in WEKA environment and then were tested in terms of accuracy, precision, TP rate, and FP rate on a 3 different sets of dataset which contains dissimilar portion of phishing and non-phishing instances. Results presented that Naïve Bayes classifier has better detection accuracy between other classifiers for predicting phishing websites while Multi-Layer Perceptron gave worst result in terms of detection accuracy. The result also showed that Support Vector machine has better FP rate between other classifier. In addition Random Forest, Decision Tree, and Naïve Bayes can classify all phishing websites as phishing correctly. It means that TP rate is 100% for these classifiers. In conclusion this project suggests using NB as the best classifier for predicting phishing and non-phishing websites. 2013 Thesis http://eprints.utm.my/id/eprint/42243/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:75244 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Zamani, Hadi Classification of phishing websites using machine learning techniques
description	Phishing detection is a momentous problem which can be deliberated by many researchers with numerous advanced approaches. Current anti-phishing mechanisms such as blacklist-base anti-phishing, Heuristic-based anti-phishing does suffer low detection accuracy and high false alarm. There is need for efficient mechanism to protect user from phishing websites. The purpose of this study is to investigate the capability of 6 machine learning algorithms which they are Multi- Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR) and Naïve Bayes (NB) to classify phishing and non-phishing websites. These algorithms were trained with two different groups of training in WEKA environment and then were tested in terms of accuracy, precision, TP rate, and FP rate on a 3 different sets of dataset which contains dissimilar portion of phishing and non-phishing instances. Results presented that Naïve Bayes classifier has better detection accuracy between other classifiers for predicting phishing websites while Multi-Layer Perceptron gave worst result in terms of detection accuracy. The result also showed that Support Vector machine has better FP rate between other classifier. In addition Random Forest, Decision Tree, and Naïve Bayes can classify all phishing websites as phishing correctly. It means that TP rate is 100% for these classifiers. In conclusion this project suggests using NB as the best classifier for predicting phishing and non-phishing websites.
format	Thesis
qualification_level	Master's degree
author	Zamani, Hadi
author_facet	Zamani, Hadi
author_sort	Zamani, Hadi
title	Classification of phishing websites using machine learning techniques
title_short	Classification of phishing websites using machine learning techniques
title_full	Classification of phishing websites using machine learning techniques
title_fullStr	Classification of phishing websites using machine learning techniques
title_full_unstemmed	Classification of phishing websites using machine learning techniques
title_sort	classification of phishing websites using machine learning techniques
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
_version_	1747816724721303552

Classification of phishing websites using machine learning techniques

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