Explore a deep learning approach for effective exploit detection using Intel® Processor Trace in this conference talk presented at the 1st Deep Learning and Security Workshop. Dive into HeNet, a hierarchical ensemble neural network that classifies hardware-generated control flow traces for malware detection. Learn how this innovative method overcomes challenges faced by static code analysis and API call-based approaches. Discover the architecture of HeNet, consisting of a low-level behavior model and a top-level ensemble model, and understand how it leverages transfer learning and image conversion techniques. Examine the evaluation results against real-world PDF reader exploits, showcasing HeNet's impressive accuracy and performance compared to classical machine learning algorithms. Gain insights into the potential of hardware trace-based malware detection and its implications for cybersecurity.