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This paper presents a workload classification framework that accurately discriminates illicit computation from authorized workloads on GPU-accelerated HPC systems at runtime. As such systems become increasingly powerful and widely-adopted, attackers have begun to run illicit and for-profit programs that typically require extremely high computing capability to be successful, depriving mission-critical and authorized workloads of execution cycles and increasing risks of data leaking and empowered attacks. Traditional measures on CPU hosts are oblivious to such attacks. Our classification framework leverages the distinctive signatures between illicit and authorized GPU workloads, and explores machine learning methods and workload profiling to classify them. We face multiple challenges in designing the framework: achieving high detection accuracy, maintaining low profiling and inference overhead, and overcoming the limitation of lacking data types and volumes typically required by deep learning models. To address these challenges, we use lightweight, non-intrusive, high-level workload profiling, collect multiple sequences of easily obtainable multimodal input data, and build recurrent neural networks (RNNs) to learn from history for online anomalous workload detection. Evaluation results on three generations of GPU machines demonstrate that the workload classification framework can tell apart the illicit workloads with a high accuracy of over 95%. The collected dataset, detection framework, and neural network models are released on github1. |
