Application of Big Data Technology to Campus Network Security Risk Assessment and Analysis

Abstract

With the continuous advancement of smart campus development, the number of terminals connected to campus networks, the diversity of business systems, and the volume of data traffic are increasing rapidly. Focusing on the operational scenario of campus network security risk assessment and analysis, this paper examines the operating mechanism of big data technology in campus network security analysis, explores its intrinsic value for risk identification and security governance, and develops an integrated application pathway from four perspectives: multisource data fusion and acquisition, risk-model optimization and construction, visualized situational assessment, and closed-loop emergency response. The proposed approach is intended to be implemented and tested using real operational data from campus networks. Practical analysis indicates that big data technology can integrate heterogeneous security data from multiple sources, reduce errors in security alerts, shift risk analysis from post-event response toward proactive prediction, and improve the intelligence level of campus network security management. The resulting practice of predictive campus network security risk assessment can provide a useful reference for university network security management.