PhishGuard: A Convolutional Neural Network Based Model for Detecting Phishing URLs with Explainability Analysis

TL;DR

PhishGuard introduces a CNN-based model for detecting phishing URLs with high accuracy and provides explainability analysis to understand feature importance, addressing limitations of previous methods with limited datasets and black-box models.

Contribution

This paper presents a novel CNN model trained on extensive data with explainability features, improving accuracy and interpretability in phishing URL detection.

Findings

Achieved 99.85% accuracy in phishing URL detection

Identified key features contributing to model decisions

Enhanced understanding of model's decision-making process

Abstract

Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black box intelligent models decision to detect suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a 1D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85%. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL.