CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks

arXiv:2402.17363v1 Announce Type: cross
Abstract: Dynamic graphs are extensively employed for detecting anomalous behavior in nodes within the Internet of Things (IoT). Generative models are often used to address the issue of imbalanced node categories in dynamic graphs. Nevertheless, the constraints it faces include the monotonicity of adjacency relationships, the difficulty in constructing multi-dimensional features for nodes, and the lack of a method for end-to-end generation of multiple categories of nodes. This paper presents a novel graph generation model, called …

abstract anomaly anomaly detection arxiv behavior constraints cs.lg cs.ro detection dynamic generative generative models graph graphs internet internet of things iot iot networks issue networks node nodes sparsity type