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This paper presents HeNet, a hierarchical ensemble neural network, applied toclassify hardware-generated control flow traces for malware detection. Deeplearning-based malware detection has so far focused on analyzing executablefiles and runtime API calls. Static code analysis approaches face challengesdue to obfuscated code and adversarial perturbations. Behavioral data collectedduring execution is more difficult to obfuscate but recent research has shownsuccessful attacks against API call based malware classifiers. We investigatecontrol flow based characterization of a program execution to build robust deeplearning malware classifiers. HeNet consists of a low-level behavior model anda top-level ensemble model. The low-level model is a per-application behaviormodel, trained via transfer learning on a time-series of images generated fromcontrol flow trace of an execution. We use Intel$^\circledR$ Processor Traceenabled processor for low overhead execution tracing and design a lightweightimage conversion and segmentation of the control flow trace. The top-levelensemble model aggregates the behavior classification of all the trace segmentsand detects an attack. The use of hardware trace adds portability to our systemand the use of deep learning eliminates the manual effort of featureengineering. We evaluate HeNet against real-world exploitations of PDF readers.HeNet achieves 100\% accuracy and 0\% false positive on test set, and higherclassification accuracy compared to classical machine learning algorithms.

HeNet: A Deep Learning Approach on Intel$^\circledR$ Processor Trace for Effective Exploit Detection