Xiaokang Zhou, Jiayi Wu, Wei Liang, Kevin I-Kai Wang, Zheng Yan, Laurence T Yang, Qun Jin
The proliferation of Internet-of-Things (IoT) technologies in modern smart society enables massive data exchange for offering intelligent services. It becomes essential to ensure secure communications while exchanging highly sensitive IoT data efficiently, which leads to high demands for lightweight models or algorithms with limited computation capability provided by individual IoT devices. In this study, a graph representation learning model, which seamlessly incorporates graph neural network (GNN) and knowledge distillation (KD) techniques, named reconstructed graph with global-local distillation (RG-GLD), is designed to realize the lightweight anomaly detection across IoT communication networks...
April 30, 2024: IEEE Transactions on Neural Networks and Learning Systems