Protect NTN-IoT Security by Malicious Traffic Detection: A Multidimensional Hypergraph Learning Approach
Xuzeng Li, Tao Zhang, Jian Wang, Zhen Han, Nan Wang, Shaohua Fan, Hongyang Du, Jiawen Kang, Jiqiang Liu, Dusit Tao Niyato · IEEE Internet of Things Journal · 2025
The vast number of devices and the complexity of requirements present significant challenges in ensuring the security of Non-Terrestrial Internet of Things (NT-IoT). Although existing studies have proposed methods like to defend against data theft and network interference attacks, there is still a need for more in-depth research on detecting data-level attacks in NTNs. Moreover, the vast and diverse nature of network traffic presents significant challenges in traffic modeling and feature extraction. Hypergraph neural networks have gained considerable attention because of capabilities in data modeling and feature extraction. However, most existing hypergraph neural networks are tailored for specific applications and are not adaptable to the detection of malicious encrypted traffic. To address these challenges, we firstly propose a hypergraph neural network-based malicious encrypted traffic detection framework to enhance the resilience of NT-IoT, enabling attack detection across unmanned aerial vehicles, base stations and satellites. Then, we introduce a Multidimensional Encrypted Traffic HyperGraph Network (METHGN). METHGN models the encrypted traffic from network, connection and time dimensions using hypergraph and uses hypergraph convolution network to extracts and fuse features. We conducted comparative experiments on IoT and The Onion Router Network encrypted traffic datasets for different classification tasks. Extensive experiments demonstrate the effectiveness and superiority of our approach.