LAUSR

In the network, different applications produce various traffic types with diverse features and service requirements. Therefore, in order to better manage and control networking, the intelligent awareness of network traffic plays a significant role. Network traffic information mainly includes service-level information (e.g., QoS/QoE), anomaly traffic detection information, etc. In this chapter, we first present a multi-level intrusion detection model framework named MSML to address these issues. The MSML framework includes four modules: pure cluster extraction, pattern discovery, fine-grained classification and model updating. Then, we propose a novel IDS framework called HMLD to address these issues, which is an exquisitely designed framework based on Hybrid Multi-Level Data Mining. In addition, we propose a new model based on big data analysis, which can avoid the influence brought by adjustment of network traffic distribution, increase detection accuracy and reduce the false negative rate. Finally, we propose an end-to-end IoT traffic classification method relying on deep learning aided capsule network for the sake of forming an efficient classification mechanism that integrates feature extraction, feature selection and classification model. Our proposed traffic classification method beneficially eliminates the process of manually selecting traffic features, and is particularly applicable to smart city scenarios.