  Anomaly event detection is crucial for critical infrastructure
security(transportation system, social-ecological sector, insurance service,
government sector etc.) due to its ability to reveal and address the potential
cyber-threats in advance by analysing the data(messages, microblogs, logs etc.)
from digital systems and networks. However, the convenience and applicability
of smart devices and the maturity of connected technology make the social
anomaly events data multi-source and dynamic, which result in the
inadaptability for multi-source data detection and thus affect the critical
infrastructure security. To effectively address the proposed problems, in this
paper, we design a novel anomaly detection method based on multi-source data.
First, we leverage spectral clustering algorithm for feature extraction and
fusion of multiple data sources. Second, by harnessing the power of deep graph
neural network(Deep-GNN), we perform a fine-gained anomaly social event
detection, revealing the threatening events and guarantee the critical
infrastructure security. Experimental results demonstrate that our framework
outperforms other baseline anomaly event detection methods and shows high
tracking accuracy, strong robustness and stability.
