Phishing Detection Using Deep Learning and Machine Learning Algorithms: Comparative Analysis

Document Type

Conference Proceeding

Source of Publication

2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)

Publication Date

11-17-2023

Abstract

Phishing attacks continue to pose a significant threat to online security, with attackers using increasingly sophisticated techniques to trick users into divulging sensitive information. In this paper, we compare the performance of two different Deep Learning (DL) models with three Machine Learning (ML) algorithms in detecting phishing attacks. The DL models include a combined model of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), as well as a Multilayer Perceptron (MLP) model. Furthermore, the ML algorithms consist of Gradient Boosting Classifier (GBC), Logistic Regression (LR), and Naive Bayes (NB). By using a public dataset of more than 10,000 websites, our performance evaluation demonstrated that the combined DL model of CNN and LSTM outperformed all of the other models and algorithms used in this study, with an accuracy of 93.1%. On the other hand, the least-performing algorithm was NB, attaining a low accuracy of 66.0%.

ISBN

979-8-3503-0460-2

Publisher

IEEE

Volume

00

First Page

0684

Last Page

0689

Disciplines

Computer Sciences

Keywords

Phishing detection, Deep learning, Machine learning, Comparative analysis, Online security

Indexed in Scopus

no

Open Access

no

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