Security Technology

We delve into how machine learning performs dynamic malware detection in a scenario where only a single malware sample is available.

Hackers use fileless threats to take advantage of existing applications and attack systems. Here we discuss noteworthy events, techniques, and best practices that can help identify fileless threats and defend against attacks.

In a new phishing campaign, audio recordings purportedly shared via OneNote were used as a lure to lead email recipients to a fake Microsoft login page that steals user account credentials.

Enterprises continue to face both old and new threats in their respective industries. This evolving world of threats emphasize the need for better perimeter security.

The rise in adoption of containers means a greater need for security awareness. Our infographic details the various threats that container users could encounter at each stage of the development pipeline.

A recently published paper warns of the prospect of advanced techniques being used to throw machine learning (ML) systems off.

Catching malware outbreaks early keeps users, communities, enterprises, and governments safe. But if malware samples are scarce, can machine learning help analyze, detect, and end an outbreak?

Fileless threats aren’t as visible compared to traditional malware and employ a variety of techniques to stay persistent. Here's a closer look at how fileless malware work and what can be done to thwart them.

Evasive network threats pose serious risks to enterprises. Learn about malicious network flow clustering—a machine learning-powered method for addressing concerns on network threats.