Malware Detection and Classification for URLs using Ensemble Learning

The advent of the digital age has brought about previously unheard levels of connectedness and information availability, as well as an increase in cyber threats. Unauthorized Uniform Resource Locators (URLs) are now a common threat and a preferred avenue for cyberattacks. Because of the inadequacy of traditional blacklist-based solutions to counteract shifting strategies, machine learning (ML) is being investigated for proactive and predictive cybersecurity measures. The goal of this research work is to improve cybersecurity defenses against URL-based vulnerabilities by utilizing ML and ensemble learning. Several machine learning methods are used, such as Random Forest, Decision Tree, Logistic Regression, XGBoost, SVM, and Naive Bayes. The intricacy of the digital threat landscape is addressed by combining the strengths of different models through the use of ensemble approaches like Weighted Voting and Soft Voting. This study highlights the significance of Weighted Voting and shows how well ensemble techniques may combine different machine learning model and emphasizes how important it is to keep improving cybersecurity defenses in line with changing threats. The results enable the possibilities for further advancements in the sector and offer workable answers to the emerging cybersecurity issues.