Accelerated data movement between data processing unit (dpu) and graphics processing unit (gpu) to address real-time cybersecurity requirements

Abstract

Apparatuses, systems, and techniques for detecting that a host device is subject to a malicious network attack using a machine learning (ML) detection system are described. A computing system includes a graphics processing unit (GPU) and an integrated circuit with a network interface, and a hardware acceleration engine. The integrated circuit hosts a hardware-accelerated security service to extract features from network data and metadata from the hardware acceleration engine and sends the extracted features to the GPU. Using the ML detection system, the GPU determines whether the host device is subject to a malicious network attack using the extracted features. The GPU can send an enforcement rule to the integrated circuit responsive to a determination that the host device is subject to the malicious network activity.