FireEye has equipped its endpoint security platform with a machine learning capability that works to identify and block cyber threats.
The company said Tuesday MalwareGuard is designed to classify malware using machine learning methods and private and public data derived from more than 15 million endpoint agents, adversarial intelligence information from a global analyst network and attack analyses.
FireEye’s team of data scientists embarked on a two-year project to research and analyze cyber threats and train the MalwareGuard platform in network threat detection through the use of public and private data.
“Reducing the window of time from discovery, to analysis and deployment of protection is critical to reducing risk in your enterprise,” commented John Laliberte, senior vice president of engineering at FireEye.
Laliberte said MalwareGuard reflects the company’s efforts to combine machine learning experience with its knowledge of threat actors to safeguard clients from “never-before-seen” cyber threats.
“Public sector organizations are always being asked to do more with less, but attackers are targeting the public sector, constantly innovating to find security gaps to exploit. Efficacy in detection is of the utmost importance to minimize the number of incidents and potential breaches where staff need to respond,” Phil Montgomery, vice president of product marketing, told ExecutiveBiz Wednesday.
Montgomery added, “FireEye’s Endpoint Security 4.5 with MalwareGuard advanced machine learning model makes intelligent classifications of undiscovered and hidden malware without human involvement. This automation helps the public sector security teams better protect themselves from zero day attacks, and allows staff to be more efficient by letting them focus on higher level tasks.”
MalwareGuard is the latest addition to the FireEye Endpoint Security platform’s integrated engines, which include the signature-based Malware Protection, behavior-based ExploitGuard and intelligence-based IOC tools.