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

• URL: http://arxiv.org/abs/2402.17363v3
• Date: Thu, 22 Aug 2024 07:45:09 GMT
• Title: CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks
• Authors: Xianshi Su, Munan Li, Runze Ma, Jialong Li, Tongbang Jiang, Hao Long,
• Abstract summary: We propose a novel graph generation model, called CGGM, specifically for generating samples belonging to the minority class. The framework consists two core module: a conditional graph generation module and a graph-based anomaly detection module. Experiments have shown that CGGM outperforms the state-of-the-art methods in terms of accuracy and divergence.
• Score: 0.6974178500813132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic graphs are extensively employed for detecting anomalous behavior in nodes within the Internet of Things (IoT). Graph generative models are often used to address the issue of imbalanced node categories in dynamic graphs. Neverthe less, 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. In this paper, we propose a novel graph generation model, called CGGM, specifically for generating samples belonging to the minority class. The framework consists two core module: a conditional graph generation module and a graph-based anomaly detection module. The generative module adapts to the sparsity of the matrix by downsampling a noise adjacency matrix, and incorporates a multi-dimensional feature encoder based on multi-head self-attention to capture latent dependencies among features. Additionally, a latent space constraint is combined with the distribution distance to approximate the latent distribution of real data. The graph-based anomaly detection module utilizes the generated balanced dataset to predict the node behaviors. Extensive experiments have shown that CGGM outperforms the state-of-the-art methods in terms of accuracy and divergence. The results also demonstrate CGGM can generated diverse data categories, that enhancing the performance of multi-category classification task.

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