From SOCs to smart cameras, AI-driven systems are transforming security from a reactive to a predictive approach. This ...

Here are some of the ways in which machine learning has contributed to cybersecurity: 1. Malware detection: Machine learning algorithms can analyze large volumes of data to identify patterns that are ...

Overview: AI-powered cloud security tools use machine learning and automation to detect threats faster than traditional ...

A primary goal of machine learning is to use machines to train other machines. But what happens if there’s malware or other flaws in the training data? Machine learning and AI developers are starting ...

MILPITAS, Calif.--(BUSINESS WIRE)--FireEye, Inc. (NASDAQ: FEYE), the intelligence-led security company, today announced the addition of MalwareGuard™ – a new advanced machine learning based detection ...

Machine learning has become an important component of many applications we use today. And adding machine learning capabilities to applications is becoming increasingly easy. Many ML libraries and ...

This article was submitted in response to the call for ideas issued by the co-chairs of the National Security Commission on Artificial Intelligence, Eric Schmidt and Robert Work. It responds to ...

Malware continues to be one of the most effective attack vectors in use today, and it is often combatted with machine learning-powered security tools for intrusion detection and prevention systems.

Contrary to what you may have read, machine learning (ML) isn't magic pixie dust. In general, ML is good for narrowly scoped problems with huge datasets available, and where the patterns of interest ...

Bottom Line: Machine learning is enabling threat analytics to deliver greater precision regarding the risk context of privileged users’ behavior, creating notifications of risky activity in real time, ...