A communication data security analysis processing method and system, and a storage medium
By using edge node monitoring and differential privacy technology, an access frequency map is generated and a target communication subset is filtered to identify abnormal communication data. This solves the problems of data leakage and long query time in the Industrial Internet of Things and achieves efficient anomaly identification and risk location.
CN122001683BActive Publication Date: 2026-06-19SHANDONG CLOUD SKY SECURITY TECH CO LTD +1
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Patent Information
- Application Number
- CN202610451981.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-08
- Publication Date
- 2026-06-19
- Estimated Expiration
- 2046-04-08
AI Technical Summary
Technical Problem
Existing technologies pose a high risk of data leakage and result in long data fragmentation and query times in scenarios such as the Industrial Internet of Things, thus affecting communication efficiency.
Method used
By generating access frequency maps through edge node monitoring, and combining differential privacy noise and gradient aggregation, target communication subsets are filtered, abnormal communication data is identified, and target scenarios are locked in through risk solving and listening blocking optimization.
Benefits of technology
It improves data processing accuracy and anomaly identification efficiency, reduces data processing interference, and ensures the continuity of communication services and environmental adaptability.
✦ Generated by Eureka AI based on patent content.
Abstract
This invention relates to the field of communication technology, specifically a communication data security analysis and processing method, system, and storage medium. The method includes: recording communication data for various IoT scenarios based on edge node monitoring; performing gradient aggregation on the edge node data and filtering target communication subsets for collaborative operation of edge nodes; extracting abnormal communication data under time-delay collaboration based on the logical coordination and temporal continuity of the target communication subsets, combined with the indirect access path composed of edge nodes and regional nodes; transforming the scenario label, node location, and time period of the abnormal communication data into constraints based on the abnormal time and abnormal response intensity associated with the abnormal communication data, and solving for risk collaboration factors when data is abnormal; monitoring scenarios where risk collaboration factors occur, and optimizing the blocking of indirect access paths when new risk collaboration factors are detected. This improves the accuracy and efficiency of communication data risk analysis.
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