MTS-IoT: A Robust Encrypted IoT Traffic Classification via Multi-dimensional Time Series
Tianye Gao, Qi Wang, Kehong Liu, Shengbao Li, Ruihai Ge, Tianning Zang · 2024
The rapid proliferation of IoT devices presents a two-fold challenge: a pronounced deficiency in security measures and a diverse array of Quality of Service (QoS) requirements. For network providers to address these challenges effectively, it is paramount first accurately to identify IoT devices. Current methodologies fall short in their robustness, owing to encrypted traffic and the intricate network environments. This paper applies multi-feature time series to the problem of encrypted IoT device traffic classification, proposing a multi-dimensional time series-based IoT traffic classification method, MTS-IoT. MTS-IoT constructs multi-dimensional time series samples from raw traffic using sliding windows of fixed packet numbers, preserving abundant information. It then utilizes a "Global-Local-Spacial" framework to deeply extract sequence features and introduces sparse self-attention to reduce the training overhead caused by multi-dimensional temporal features. Comprehensive experiments were conducted on a renowned dataset, in which we retained the traffic of non-IoT devices to simulate the real world. Experiments indicate that MTS-IoT outperforms existing methods in classification performance, pushing the F1 to 98.63%(4.46%↑). Moreover, it can achieve accurate detection throughout the entire traffic cycle, resist network congestion, and resist traffic shaping, underscoring its robustness in diverse scenarios.