Deep neural network based malware detection using two dimensional binary program features

In this paper we introduce a deep neural network based malware detection system that Invincea has developed, which achieves a usable detection rate at an extremely low false positive rate and scales to real world training example volumes on commodity hardware. We show that our system achieves a 95% detection rate at 0.1% false positive rate (FPR), based on more than 400,000 software binaries sourced directly from our customers and internal malware databases. In addition, we describe a non-parametric method for adjusting the classifier's scores to better represent expected precision in the deployment environment. Our results demonstrate that it is now feasible to quickly train and deploy a low resource, highly…