GLADS: A global-local attention data selection model for multimodal multitask encrypted traffic classification of IoT

With the rapid development of the Internet of Things (IoT), numerous of IoT devices and different characteristics in IoT traffic patterns need traffic classification to enable many important applications. Deep-learning-based (DL -based) traffic methods have gained increasing attention due to their high accuracy and because manual feature extraction is not needed. Furthermore, seek a lightweight, multitask methods that supports a "performance -speed" trade-off. Thus, we proposed the 0.11 M global-local attention data selection (GLADS) model. The core of the GLADS model includes an "indicator" mechanism and a "local + global" framework. The "indicator" mechanism is a completely different method for handling multimodal input that allows the model to efficiently extract features from multimodal input with a single-modal-like approach. The "local + global" framework for the "performance-speed" trade-off includes a "local" part to obtain the features of each patch in the model input and a Global-Local Attention mechanism in the "global" part outputs the classification results under all possible lengths. Tests on the ISCX-VPN-2016, ISCX-Tor-2016, USTC-TFC-2016, and TON_IoT datasets show that GLADS achieves better performance than several state-of-the-art baselines, ranging from 2.42% to 7.76%. Furthermore, we also propose the "indicator," which allows the model to simply cope with multimodal input. Based on global -local attention, we analyze the relation of the input section and model performance in detail.