In order to thwart potential cyberattacks and better protect businesses and individuals against cyberthreats, security players are closely monitoring developments in AI.
Introduction The use of deep neural networks has significantly improved the performance of machine learning in fields such as image […]
Introduction Many organizations are transitioning their security management to the cloud, where it is much easier to collect and access […]
Machine learning (ML) used for static portable executable (PE) malware detection typically employs per-file numerical feature vector representations as input […]
Although powerful for conviction of malicious artifacts, machine learning based detection do not generally produce further information about the type […]
Any good attack-chain usually involves tricking users at some point, whether it’s getting them to run a malicious file because […]
In the wild, we often see that malware in user systems persists well hidden in obfuscated or randomized file locations. […]
Generating up to date, well labeled datasets for machine learning (ML) security models is a unique engineering challenge, as large data volumes, complexity of labeling, and constant concept drift makes it difficult to generate effective training datasets. Here we describe a simple, resilient cloud infrastructure for generating ML training and testing datasets, that has enhanced the speed at which our team is able to research and keep in production a multitude of security ML models.
One of the best-known secrets of machine learning (ML) is that the most reliable way to get more accurate models […]