Detection of phishing websites using machine learning approaches

Farashazillah Yahya and Magnus Anai and Ryan Isaac W Mahibol and Sidney Allister Frankie and Rio Guntur Utomo and Chong Kim Ying and Eric Ling Nin Wei (2021) Detection of phishing websites using machine learning approaches.

[img] Text
Detection of phishing websites using machine learning approaches.ABSTRACT.pdf

Download (63kB)
[img] Text
Detection of phishing websites using machine learning approaches.pdf
Restricted to Registered users only

Download (928kB) | Request a copy


As the world responded to the Coronavirus Disease 2019 (COVID-19) pandemic in 2020, digital operations became more important, and people started to depend on new initiatives such as the cloud and mobile infrastructure. Consequently, the number of cyberattacks such as phishing has increased. Phishing websites can be detected using machine learning by classifying the websites into legitimate or illegitimate websites. The purpose of the study is to conduct a mini-review of the existing techniques and implement experiments to detect whether a website is malicious or not. The dataset consists of 11,055 observations and 32 variables. Three supervised learning models are implemented in this study: Decision Tree, K-Nearest Neighbour (KNN), and Random Forest. The three algorithms are chosen because it provides a better understanding and more suitable for the dataset. Based on the experiments undertaken, the result shows Decision Tree has an accuracy of 91.16% which is the lowest compared to the other models, 97.6% for the KNN model which is the highest among all the models and 94.44% accuracy for the Random Forest model. Through comparisons between the three models, KNN was the prime candidate for the best model considering that it has the highest accuracy. However, Random Forest is deemed more suitable for the dataset even though the accuracy is lesser because of the lowest false-negative value than the other models. The experiments can be further investigated with different datasets and models for comparative analysis.

Item Type: Proceedings
Keyword: Website , Phishing , Malicious URL , Prediction , Machine learning
Subjects: Q Science > Q Science (General) > Q1-390 Science (General) > Q1-295 General
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Department: FACULTY > Faculty of Computing and Informatics
Depositing User: DG MASNIAH AHMAD -
Date Deposited: 03 May 2022 21:27
Last Modified: 03 May 2022 21:27

Actions (login required)

View Item View Item