This paper presents HeNet, a hierarchical ensemble neural network, applied to classify hardware-generated control flow traces for malware detection. Deep learning-based malware detection has so far focused on analyzing executable files and runtime API calls. Static code analysis approaches face challenges due to obfuscated code and adversarial perturbations. Behavioral data collected during execution is more difficult to obfuscate but recent research has shown successful attacks against API call based malware classifiers. We investigate control flow based characterization of a program execution to build robust deep learning malware classifiers.HeNet consists of a low-level behavior model and a toplevel ensemble model. The low-level model is a per-application behavior model, trained via transfer learning on a time-series of images generated from control flow trace of an execution. We use Intel R Processor Trace enabled processor for low overhead execution tracing and design a lightweight image conversion and segmentation of the control flow trace. The top-level ensemble model aggregates the behavior classification of all the trace segments and detects an attack. The use of hardware trace adds portability to our system and the use of deep learning eliminates the manual effort of feature engineering.We evaluate HeNet against real-world exploitations of PDF readers. HeNet achieves 100% accuracy and 0% false positive on test set, and higher classification accuracy compared to classical machine learning algorithms.

This document specifies PB-

This document describes a Policy Information Base (PIB) for a device implementing the Differentiated Services Architecture. The provisioning classes defined here provide policy control over resources implementing the Differentiated Services Architecture. These provisioning classes can be used with other none Differentiated Services provisioning classes (defined in other PIBs) to provide for a comprehensive policy controlled mapping of service requirement to device resource capability and usage.

No abstract

Dynamic taint analysis (DTA) is