Proteus: A Difficulty-Aware Deep Learning Framework for Real-Time Malicious Traffic Detection

Deep learning (DL) has been recently used for malicious traffic detection. However, DL models are often faced with a dilemma between model size and performance: larger models have better accuracy, but suffer from high detection latency, which severely impacts realtime traffic performance, while lightweight models have low detection latencies, but sacrifice accuracy. In this paper, we introduce Proteus, a swift and precise attack detection framework that adaptively adjusts DL models in real-time based on sample detection difficulty. To address diverse detection difficulties in traffic data, we devise a Double Dynamic Convolutional Neural Network (DDCN) with two pivotal modules: the Dynamic Feature Campaign (DFC) and the Tailor Module (TM). DFC enables the model to discern and accentuate the most influential features, while TM autonomously gauges sample difficulty, cropping the overall model. We further design an auxiliary detection module to streamline the detection, especially for network devices like routers lacking GPUs but equipped with multiple CPU cores. Experiments on different network devices show that Proteus completes the detection of each flow within 0.6 ms, and achieves 99.34% detection accuracy, outperforming other solutions.