Security Technology

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.

Here’s a closer look at the challenges enterprises are confronted with when adopting a more robust cybersecurity strategy, and how managed detection and response can help address them.

How can DevOps — complemented with multilayered security solutions — align with the GDPR’s mandate of data protection by design and default?

Threat data — enough of it — is critical to a machine learning system’s success in cybersecurity solutions. But is data quantity the be-all and end-all of effective machine learning?

Addressing the need for a more efficient way to defend against spam in the early 2000s, the antispam industry turned to machine learning. The effect: Overall cyberdefense was enhanced to catch approximately 95 percent of spam.

Here are some considerations and best practices that developers, IT operations professionals, and system administrators should take into account in securing the infrastructures that power the applications they use.

While the GDPR will require businesses to comply with rules that will ultimately protect customer data, concerns have been raised regarding how it will tackle automation in data analytics as the market for artificial intelligence (AI) continues to grow.

Because most malware threats come from software download events, Trend Micro subsequently developed a human-readable machine learning system that successfully classifies unknown files into either benign or malicious in nature.