With the rapid development of cloud-network convergence, alert data processing has become a critical task in intelligent cloud-network operations and maintenance. Enterprise threat detection systems serve as a vital defense against cyberattacks. However, due to well-known challenges such as massive unlabeled data, false alerts, and lack of interpretability, threat detection remains a formidable challenge. To address these issues, we propose the Enterprise Threat Detection Explainer (ETDE), an interpretable graph neural network (GNN)-based framework designed to automatically identify attackers from intrusion prevention system (IPS) alerts. ETDE extracts relevant subgraph structures and features from security attribute graphs to model attack patterns, while providing comprehensive explanations to help security teams rapidly pinpoint threats and accelerate incident response. Evaluations on real-world operational data demonstrate that ETDE significantly reduces the manual workload for security analysts, enhancing both efficiency and reliability in threat detection.

Abstract In recent years, many few‐shot classification methods have been proposed. However, only a few of them have explored robust classification, which is an important aspect of human visual intelligence. Humans can effortlessly recognise visual patterns, including lines, circles, and even characters, from image data that has been corrupted or degraded. In this paper, the authors investigate a robust classification method that extends the classical paradigm of robust geometric model fitting. The method views an image as a set of points in a low‐dimensional space and analyses each image through low‐dimensional geometric model fitting. In contrast, the majority of other methods, such as deep learning methods, treat an image as a single point in a high‐dimensional space. The authors evaluate the performance of the method using a noisy Omniglot dataset. The experimental results demonstrate that the proposed method is significantly more robust than other methods. The source code and data for this paper are available at https://github.com/pengsuhua/PMF_OMNIGLOT .

Due to the difference in high-rise distance, laser incident angle, and occlusion, incomplete point cloud of building facades is incomplete. The existing point cloud facade completion work does not fully explore the semantic constraints. Thus, it is difficult to deal with the facades with large-area missing. This paper proposes a novel facade completion framework for point clouds. By introducing semantic constraints and semantic-synchronized GAN, we solve the facade completion problem with large-area missing while the semantic consistency is augmented.