Empowering Cyber Defense: Advanced Machine Learning for Detecting Malicious URLs - Raju Anitha, Oduri Jahnavi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This paper explores a novel strategy for malware detection by imposing the abilities of machine learning algorithms, in specific Random Forest and Decision Trees. Our research revolves around the development of a model that assesses URLs to determine their trustworthiness and identifies malware originating from various online sources. By integrating custom modifications into the Random Forest and Decision Tree algorithms, our model achieves enhanced sensitivity to the nuanced indicators of malicious content. This approach not only facilitates the detection of malware through URL analysis but also extends to the scrutiny of files downloaded from the internet, providing a comprehensive solution for cybersecurity threats. A key innovation in our method is the application of advanced feature selection techniques, which significantly improve the model's accuracy and efficiency in identifying potential threats. Our findings indicate a substantial improvement in malware detection rates, setting a new precedent in the field of cybersecurity. This study contributes to the ongoing efforts in digital security, offering a robust tool for the early detection and mitigation of malware risks.