Classification of Spam URLs Using Machine Learning Approaches

TL;DR

This paper evaluates various machine learning models for classifying URLs as spam or nonspam, finding that bagging achieves the highest accuracy and outperforms current state-of-the-art methods.

Contribution

It introduces a feature extraction approach from URLs and demonstrates that bagging significantly improves spam detection accuracy over existing methods.

Findings

Bagging achieved 98.64% accuracy in spam URL classification.

Bagging outperformed other models and current state-of-the-art approaches.

The study highlights the effectiveness of ensemble methods for spam detection.

Abstract

The Internet is used by billions of users every day because it offers fast and free communication tools and platforms. Nevertheless, with this significant increase in usage, huge amounts of spam are generated every second, which wastes internet resources and, more importantly, users' time. This study investigates the use of machine learning models to classify URLs as spam or nonspam. We first extract the features from the URL as it has only one feature, and then we compare the performance of several models, including k nearest neighbors, bagging, random forest, logistic regression, and others. Experimental results demonstrate that bagging outperformed other models and achieved the highest accuracy of 98.64%. In addition, bagging outperformed the current state-of-the-art approaches which emphasize its effectiveness in addressing spam-related challenges on the Internet. This suggests that bagging is a promising approach for URL spam classification.