Attackers inject malicious code into AI models hosted on the public repositories. These models allow attackers to manipulate ...

To detect previously unseen malware, ML models analyze system ... use machine learning. Today, attackers can manipulate some machine learning models by feeding them misleading data to evade detection.

In their study, authors developed a machine learning algorithm ... by contributing institutions or for the use of any information through the EurekAlert system.

New Android malware campaigns use Microsoft's cross-platform framework .NET MAUI while disguising as legitimate services to evade detection. The tactic was observed by McAfee's Mobile Research ...

A University of Cincinnati study found machine learning models can aid in the automation and detection of abnormal ... SD events that were not identified using human scoring, likely due to a ...

Cybersecurity researchers have found that it's possible to use large language models (LLMs ... power of LLMs to iteratively rewrite existing malware samples with an aim to sidestep detection by ...

Latrodectus is a versatile malware family that employs advanced tactics to infiltrate systems, steal sensitive data, and evade detection ... s objectives and the system it has compromised.

This project utilizes machine learning techniques to build a robust malware detection system capable of analyzing files to identify ... to ensure consistency and includes features extracted using ...

This comprehensive exploration delves into the crucial role of machine learning in the detection of malware, unraveling the capabilities of five key algorithms that have become the vanguard in the ...