Performance Exploration of ML and DL Techniques for Malicious URL Detection: A PSO Based Feature Selection Approach

Social networks are ubiquitous, with billions of users worldwide. These channels are essential for communication and information sharing in modern life. Due to their popularity, fraudsters have targeted online platforms using fraudulent URLs. Traditional URL-classification methods, which use post-access features or static analysis are not able to keep up with hackers’ ever-changing strategies and lack the granularity needed for precise URL categorization. To address this issue, we have proposed a unique approach to detect malicious URL. We have developed an ensemble machine learning approach comprising of Decision Tree, Random Forest, Extra Tree, and XGboost along with feature selection with the help of Particle Swarm Optimization (PSO). Feature selection refines model performance and reduce computation complexity. In this working scheme, we have implemented different machine learning and deep learning techniques to evaluate and compare the methods’ accuracy and effectiveness with the proposed scheme. We have utilized ISCX-URL2016 dataset and achieved an accuracy of 98.46% by the proposed ensemble machine learning. By this experiment, we have observed that our proposed machine learning model outperforms deep learning techniques and the existing methods as well. Additionally, we have analyzed the runtime and figured out that our proposed scheme computes effectively within 56.98s as total execution time with the selected features.