Malicious URL Detection: Boosting Algorithm

Abstract

The rising prevalence of cyber threats, including phishing, malware, and fraud, has made the detection of malicious URLs a critical task in cybersecurity. This study focuses on the application of boosting algorithms specifically Gradient Boosting, XGBoost, and AdaBoost to tackle the challenge of detecting malicious URLs within imbalanced datasets. By leveraging an ensemble approach that combines multiple weak learners, these algorithms aim to enhance classification accuracy. To mitigate class imbalance, the research integrates advanced techniques such as the Synthetic Minority Oversampling Technique (SMOTE) and cost-sensitive learning. A comprehensive evaluation using key performance metrics including accuracy, precision, recall, and F1-score. The results are anticipated to demonstrate that boosting algorithms not only achieve high classification accuracy but also show robustness in identifying malicious URLs, even in highly imbalanced datasets. The findings highlight the potential of boosting algorithms to enhance threat detection systems and contribute to the evolving landscape of automated cybersecurity solutions.