Published By: Symantec
Published Date: Dec 13, 2017
Security teams face sophisticated attacks that ‘hide in plain sight’ and often dwell in customer environments as long as 190 days1. And attackers increasingly employ stealthy techniques to move freely within a customer environment like using stolen credentials to masquerade as legitimate users. There has been a marginal decline in zero-day discoveries and an increase in ‘living off the land’ tactics that don’t rely on the traditional combination of vulnerabilities followed by malware. These tactics are more difficult to detect since they make use of legitimate tools.
Today’s anti-malware solutions running as applications above the operating system are no match for the stealth techniques used by today’s malware developers. Hardware-assisted security products take advantage of a “deeper” security footprint.
As malware becomes ever more sophisticated and evasive, new technologies are emerging to uncover threats no matter how well they’re camouflaged. This paper proposes a logical design strategy for dynamic malware analysis that optimizes detection effectiveness, efficiency, and economics.
Overwhelmed by the volume of security intelligence and alerts, human analysts need machine learning to augment and accelerate efforts. Machine learning moves security analytics from diagnostic and descriptive to prescriptive and proactive, leading to faster and more accurate detection.
AV-TEST did a review of McAfee Web Protection in the Cloud vs. Websense Cloud Security 2014 and tested each against a set of malicious URLs with zero-day malware to determine performance against the latest web threats.