Learning Hypersphere for Few-shot Anomaly Detection on Attributed Networks
Qiuyu Guo, Xiang Hui Zhao, Yang Fang, Shiyu Yang, Xuemin Lin, Dian Ouyang · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
The existence of anomalies is quite common, but they are hidden within the complex structure and high-dimensional node attributes of the attributed networks. As a latent hazard in existing systems, anomalies can be transformed into important instruction information once we detect them, e.g., computer network admins can react to the leakage of sensitive data if network traffic anomalies are identified. Extensive research in anomaly detection on attributed networks has proposed various techniques, which do improve the quality of data in networks, while they rarely cope with the few-shot anomaly detection problem. Few-shot anomaly detection task with only a few dozen labeled anomalies is more practical since anomalies are rare in number for real-world systems.