This paper seeks to conduct a thorough systematic literature review (SLR) and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen ...

Malware analysis and detection techniques include employing a malware honeypot, machine learning behavioral analysis, or using Nmap to help detect and mitigate it. The first stealth virus, ‘Brain’ ...

Artificial Intelligence (AI) provides a promising solution by automating and improving malware detection through the use of machine learning and deep learning models. This project explores the ...

The solution optimizes performance by pre-filtering files using machine learning and HyperDetect technology, sending only files requiring further analysis to the sandbox. After detonation, Bitdefender ...

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 ...

Using supervised learning and hybrid methodologies, it was discovered that the classification algorithms for FDI attack detection improved. The real-time smart grid datasets that can be used to ...

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 ...

We use a variety of machine learning models that use different algorithms to predict whether a certain file is malware. Some of these algorithms are binary classifiers that give a strict ...

This paper seeks to conduct a thorough systematic literature review (SLR) and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen ...