Innovative Approaches to Malicious Url Detection: Using Machine Learning Unleashed

Innovative Approaches to Malicious Url Detection: Using Machine Learning Unleashed

Patlolla Varshini Reddy Computer Science and Engineering

(Cyber Security)

Institute of Aeronautical Engineering Dundigal, Hyderabad 22955A6206@iare.ac.in

Mr.Y.Manohar Reddy Associate Professor

Computer Science and Engineering (Cyber Security)

Institute of Aeronautical Engineering Dundigal, Hyderabad y.manoharreddy@iare.ac.in

Rathod Praveen

Computer Science and Engineering (Cyber Security)

Institute of Aeronautical Engineering Dundigal, Hyderabad 22955A6203@iare.ac.in

Mohammad Asif

Computer Science and Engineering (Cyber Security)

Institute of Aeronautical Engineering Dundigal, Hyderabad 22955A6202@iare.ac.in

ABSTRACT

The proliferation of malicious URLs presents significant challenges to cyber security, necessitating the development of advanced detection techniques. Using the capabilities of Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) models, this study investigates novel machine learning techniques for identifying dangerous URLs. The effectiveness of each model in differentiating between benign and malicious URLs is assessed, taking into account a range of performance indicators including accuracy, precision, recall, and F1-score. The integration of feature extraction techniques and robust data preprocessing enhances the models' ability to generalize across diverse URL data sets. This study demonstrates how machine learning may be used to strengthen defenses against cyber attacks and lays the groundwork for future  developments in the detection of dangerous URLs.

Keywords: Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF).