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As the key step of anomaly detection for multivariate time-series (MTS) data, learning the relations among different variables has been explored by many approaches. However, most existing approaches overlook the heterogeneity among variables, that is, different types of variables (continuous numerical variables, discrete categorical variables or hybrid variables) may have different edge distributions. In this paper, we propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS. Specifically, we first combine the embedding similarity subgraph generated by sensor embedding and the feature value similarity subgraph generated by sensor values to construct a time-series heterogeneous graph, which fully utilizes the rich heterogeneous mutual information among variables. Then, a prediction model containing nodes and channel attentions is jointly optimized to obtain better time-series representations. This approach fuses the state-of-the-art technologies of heterogeneous graph structure learning (HGSL) and representation learning. Experimental results on four sensor datasets from real-world applications demonstrate that our approach achieves more accurate anomaly detection compared to baseline methods, laying a foundation for the rapid positioning of anomalies. [ABSTRACT FROM AUTHOR]

Anomaly detection is distinguishing unusual objects from normal patterns. It is a complex task due to unpredictable nature of anomalies, which can appear in many forms or they can be hidden by mimicking normal behaviors in a graph structure. Such diversity makes this Deep learning approaches can solve these problems by extracting complex patterns from networks. However, addressing different forms of anomalous instances is essential for successfully implementing these approaches, as different anomaly types require further analysis. Additionally, it is challenging to interpret anomalies beforehand without focusing on every aspect of anomalies. Our objective is to propose an architecture capable of handling all types of anomalous entities by tackling challenges across various domains. In this paper, we introduce ARNAD, a novel framework that integrates three deep models to identify anomalies in graphs: graph neural network, autoencoder, and adversarial autoencoder. ARNAD approaches graph anomaly detection by utilizing the features of the deep parts, and four key elements stand out: (1) the autoencoder learns the overall graph structure and identifies highly deviated ones, (2) the graph neural network exploits graph structure to detect anomalies among the communities, (3) a fixed-size randomized neighborhood that prevents overfitting while reducing complexity (4) the adversarial autoencoder improves the robustness of the framework and discriminates anomalies. To detect anomalies, four receptive components assign risk scores to objects in the attributed network. We evaluated the framework with three synthetic datasets that simulate different behaviors of anomalies and six widely used real attributed networks. Our experimental results show that ARNAD performs competitively with other state-of-the-art models in detecting anomalous entities while minimizing false positives, demonstrating ARNAD's effectiveness in detecting graph anomalies. [ABSTRACT FROM AUTHOR]

Attribute network anomaly detection has attracted growing interest in recent years, which aims to separate the points whose behavior is clearly different from others. The complex interactions between the network structure and node attributes result in difficulty in detecting anomalous nodes on attribute networks. To alleviate the above mentioned issue, in this paper, we design a deep joint adversarial learning representation framework (JAANE) for attribute network anomaly detection, by capturing the consistency and complementarity between network structure and node attributes. Specifically, JAANE utilizes a weight-sharing encoder to learn the attribute embedding and structure embedding in a shared latent space. Then, the feature fusion module fuses the learned attribute embedding and structure embedding into the fused node embedding to capture the consistency and complementarity between them. Finally, the fused node embedding is regularized via adversarial learning, and the anomaly nodes outside the regularized hypersphere space can be effectively detected. The experiment results on the real-world datasets indicate that the proposed JAANE performs better than other state-of-the-art, which demonstrates the effectiveness of the proposed method. The source code and data are released in https://haoyfan.github.io/. • We introduce JAANE, a framework for attribute network anomaly detection. It jointly learns attribute and structure embeddings, fusing them into a shared latent space. JAANE then identifies abnormal nodes using a compact hypersphere of normals. • We design a feature fusion module to capture attribute-structural consistency and complementarity. It employs multiplication-based fusion for attribute-network alignment and addition-based fusion for capturing attribute-structural complementarity. • Experimental evaluations on real-world attribute network datasets demonstrate JAANE's superior performance in anomaly detection compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]