Malware detection is a vital think about the protection of the Personal computer systems. However, presently using signature-based strategies cannot offer correct detection of zero-day attacks and ...

Although not widely implemented, the concept of machine learning methods for malware detection is not new. Several types of studies were carried out in this field, aiming to figure the accuracy of ...

This research explores the issues that modern digital environments face due to advanced and widespread malware. The study focuses on issues related to feature selection, model efficiency, and the ...

Machine learning algorithms automate and enhance malware analysis processes. Based on patterns and anomalies, machine learning enables the detection of previously unknown threats.

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 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 ...

Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models ...

UC San Francisco researchers have found a way to double doctors’ accuracy in detecting the vast majority of complex fetal heart defects in utero – when interventions could either correct them or ...