Phishing Website Detection Using a Combined Model of ANN and LSTM

TL;DR

This paper proposes a combined ANN and LSTM model to detect phishing websites, aiming to improve cybersecurity by accurately identifying fake web pages using machine learning techniques.

Contribution

It introduces a novel hybrid model combining ANN and LSTM for phishing website detection, leveraging deep learning for enhanced accuracy.

Findings

The combined model outperforms individual models in detection accuracy.

High precision and recall achieved in identifying phishing sites.

Effective use of secondary dataset demonstrates model robustness.

Abstract

In this digital era, our lives highly depend on the internet and worldwide technology. Wide usage of technology and platforms of communication makes our lives better and easier. But on the other side it carries out some security issues and cruel activities, phishing is one activity of these cruel activities. It is a type of cybercrime, which has the purpose of stealing the personal information of the computer user, and enterprises, which carry out fake websites that are the copy of the original websites. The attackers used personal information like account IDs, passwords, and usernames for the purpose of some fraudulent activities against the user of the computer. To overcome this problem researchers focused on the machine learning and deep learning approaches. In our study, we are going to use machine learning and deep learning models to identify the fake web pages on the secondary dataset.