Browser Extension for Fake URL Detection

TL;DR

This paper introduces a browser extension that employs machine learning models to detect malicious URLs, spam emails, and analyze network logs, significantly enhancing online security with high accuracy.

Contribution

The paper presents a novel browser extension integrating three machine learning-based functionalities for cyber attack detection, with models trained on extensive datasets achieving high accuracy.

Findings

LGBM classifier achieved 96.5% accuracy in phishing URL detection.

Multinomial NB model achieved 97.09% accuracy in spam email detection.

The extension effectively enhances online security through machine learning.

Abstract

In recent years, Cyber attacks have increased in number, and with them, the intensity of the attacks and their potential to damage the user have also increased significantly. In an ever-advancing world, users find it difficult to keep up with the latest developments in technology, which can leave them vulnerable to attacks. To avoid such situations we need tools to deter such attacks, for this machine learning models are among the best options. This paper presents a Browser Extension that uses machine learning models to enhance online security by integrating three crucial functionalities: Malicious URL detection, Spam Email detection and Network logs analysis. The proposed solution uses LGBM classifier for classification of Phishing websites, the model has been trained on a dataset with 87 features, this model achieved an accuracy of 96.5% with a precision of 96.8% and F1 score of 96.49%. The Model for Spam email detection uses Multinomial NB algorithm which has been trained on a dataset with over 5500 messages, this model achieved an accuracy of 97.09% with a precision of 100%. The results demonstrate the effectiveness of using machine learning models for cyber security.