Optimizing Malicious Website Detection Through Comparative Analysis of Machine Learning Techniques

Abstract

The improvement of malware data exploitation risks, which appeared due to malicious websites, as well as an increase in their frequency, is results of modern threats. Modern methods for malicious website detection display a bad performance, producing multiple incorrect alarms, but fail to identify contemporary security threats correctly. More advanced malware website identification techniques are based on XGBoost systems combined with AdaBoost and Random Forest. The framework is composed of four phases: (1) Data Acquisition and Preliminary Analysis, utilizing a Kaggle dataset to discern key patterns; (2) Data Preprocessing and Model Implementation, which consists of data cleaning, normalization, and segmentation to train the model effectively; (3) Detection and Classification Evaluation, which computes performance metrics like precision, recall, F1-score, and accuracy; and (4) Comparative Analysis, where XGBoost outperforms traditional methods. The XGBoost model had a detection accuracy of 86.60% in its practice run since it generated less wrong outputs to show its capability in malware URL detection. Cybersecurity research needs machine learning in threat detection in order to eradicate human-based new threat evaluation processes and to demonstrate the need for sophisticated machine learning frameworks. The development of proven modern theoretical algorithms in malicious website detection should be researched upon because these algorithms show better effectiveness in research work.