Lightweight Hybrid Device Identification for IoT Applications

The rapid proliferation of Internet of Things (IoT) devices has increased the variety of devices and data traffic, making data management and analysis more complex. This complexity has raised the demand for efficient device identification methods to ensure the smooth operation of the network. Conventional identification methods rely on machine learning (ML) and deep learning (DL), which either suffer from unstable feature engineering or rely on large labeled datasets with confined representation. To overcome these shortcomings, generic hybrid representations of raw traffic are essential for precise device identification. Additionally, existing work mainly investigated device identification in clouds, incurring high network latency and computation costs. A few studies have identified IoT devices in edge, but such methods used simple neural networks, resulting in incomplete representation and redundant operations. Comprehensive representations typically require complex models, but the limited resources at the edge are insufficient to execute these models. Therefore, this article proposes a lightweight hybrid device identification (LHDI) approach, which achieves efficient device identification in resource-constrained edge nodes. First, we adopt the unsupervised pretraining to enhance the characterization of network packets. Second, we devise LHDI by integrating bidirectional long short-term memory (Bi-LSTM) and Transformer-based blocks in a parallel configuration. Third, a pruning framework is introduced to automatically reduce Transformer parameters using structured sparsity methods without retraining. By reducing redundant neural network parameters, the proposed lightweight model facilitates effective device identification in edge, without losing representation capabilities. Experimental results demonstrate that our methods deliver high accuracy with low cost compared to others.