Open Access Open Access  Restricted Access Subscription Access

Whaling Guard : Phishing Detection Using Machine Learning

Adithyan M S, Harikrishnan K B, Aswani N K, Amal Soman, Krishnapriya V J

Abstract


Phishing is a type of cyberattack that aims to steal sensitive information such as usernames, passwords, and credit card details. Phishing attacks have become increasingly sophisticated and prevalent in today’s digital world. Thus, the development of the phishing detection model is essential to combat the increasing threat of phishing attacks, protect users from fraud and data breaches, and enhance overall cybersecurity in the digital landscape. Several machine learning algorithms have been proposed to detect phishing websites by analyzing various features. In this research paper, we compare several machine learning algorithms to detect phishing websites using a dataset of 545,895 samples which is created by pre-processing and merging two separate datasets. We use 74 features to train and evaluate the algorithms, and we found that Logistic Regression (LR) achieved the highest accuracy of 94.44% after comparing models. The trained LR model is integrated into our WhalingGuard. Web Application for predicting whether the input URL from the user interface is phishing or non-phishing.


Full Text:

PDF

References


Michael A. Ivanov, Bogdana V. Kliuchnikova, Ilya V. Chugunkov, Anna

M. Plaksina, “Phishing Attacks and Protection Against Them,” 2021.

Michael A. Ivanov, Bogdana V. Kliuchnikova, Ilya V. Chugunkov, Anna

M. Plaksina , “Survey on Detection and Prevention of Phishing Websites using Machine Learning,” 2021.

Hossein Abroshan, Jan Devos, Geert Poels, Eric Laermans , “Phishing Happens Beyond Technology: The Effects of Human Behaviors and Demographics on Each Step of a Phishing Process,” 2021.

M. D. Bhagwat, P. H. Patil, T. S. Vishawanath, “A Methodical Overview on Detection, Identification and Proactive Prevention of Phishing Web- sites ,” 2021.

Yi Wei, Yuji Sekiya , “Sufficiency of Ensemble Machine Learning Methods for Phishing Websites Detection,” 2021.

N Kumaran, Purandhar Sri Sai, Lokesh Manikanta. , “Web Phishing Detection Using Machine Learning,” 2022.

Paulius Vaitkevicius, “Comparison of Classification Algorithms for Detection of Phishing Websites,” 2020.

Sindhu, Sunil Parameshwar Patil, Arya Sreevalsan, Faiz Rahman, “Phishing Detection using Random Forest, SVM and Neural Network with Backpropagation,” 2020.

Maria Sameen, Kyunghyun Han, Seong Oun Hwang , “PhishHaven—An Efficient Real-Time AI Phishing URLs Detection System ,” 2020.

Ilker Kara, Murathan Ok, Ahmet Ozaday, “Characteristics of Under- standing URLs and Domain Names Features: The Detection of Phishing Websites With Machine Learning Methods,” 2022.


Refbacks

  • There are currently no refbacks.