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

Abstract

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 top-level 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${}^{\circledR }$ 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.

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Cited by in corpus (4)

• Deep Transfer Learning for Static Malware Classification
• Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions
• To believe or not to believe: Validating explanation fidelity for dynamic malware analysis
• Capturing the symptoms of malicious code in electronic documents by file's entropy signal combined with Machine learning