As the landscape of malware threats evolves., the need for effective detection methods becomes paramount. This study evaluates the performance of five ML algorithms Random Forest., SVM., DT., KNN., ...

This paper presents an extensive study of malware detection using machine learning techniques. Machine learning algorithms, including adaboost ensemble learning, stacking ensemble learning, hard ...

Leveraging the power of Machine Learning as a tool, we delve into the realm of app permissions to discern the true nature of applications, whether they harbor malicious or benign intent. By analyzing ...

Throughout this project, I delve into a myriad of machine learning techniques, from classic algorithms to state-of-the-art deep learning architectures. By leveraging the power of decision trees, ...

Using unsupervised machine learning techniques, security experts can cluster URLs or domains to identify DGAs (domain generation algorithms), used by malware creators to generate domains that act as ...

Researchers used 60% of the known malware samples to train the original DNN algorithm, ... The research is part of Microsoft's recent efforts of improving malware detection using machine learning ...