Collaborative Learning Network Security Awareness Model
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Solution Overview
Problem
In large-scale complex heterogeneous networks, determining the overall network security situation is challenging due to the multitude of monitoring points and varying security events, as traditional methods rely on expert analysis and limited data, which can lead to inaccuracies and inefficiencies.
Innovation Solution
A network security situation awareness method based on collaborative learning, integrating data from different monitoring points, using a convolutional neural network for feature extraction, an attention mechanism for personalized customization, and a fully connected network for grading, while improving generalization through a collaborative learning framework to create a unified model for enhanced awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional expert analysis methods are used to determine overall network security situation, then the method is simple to implement, but the accuracy and effectiveness are limited due to expert knowledge limitations and data constraints
Solution Approach 1:
The patent replaces the mechanical system of expert human analysis with an automated intelligent system based on deep learning models. The convolutional neural network automatically extracts features from security event data, the attention mechanism dynamically weights important features, and the fully connected network grades security situations, eliminating the need for manual expert judgment while improving accuracy and consistency.
Solution Approach 2:
The patent transforms the analysis from qualitative expert judgment to quantitative automated grading. By changing the parameter space from human expertise to machine learning models with adjustable parameters (convolutional kernels, attention weights, grading thresholds), the system achieves higher measurement precision while maintaining implementability through standardized processing pipelines.
2Measurement precision
If data from multiple network units is integrated to improve awareness accuracy, then the measurement precision improves, but the data privacy and security risks increase
Solution Approach 1:
The patent segments the data processing into two distinct phases: local processing at each network unit where raw security event data is processed through the convolutional neural network to extract features, and then only the extracted feature representations are shared and integrated. This segmentation ensures that raw sensitive data remains localized while still enabling improved awareness accuracy through feature-level integration across network units.
Solution Approach 2:
The patent introduces an intermediary layer of feature extraction and representation transformation. Instead of directly integrating raw security event data from multiple network units (which would expose sensitive information), the system first transforms data into abstract feature representations through the convolutional neural network. These features serve as intermediaries that capture security patterns without exposing underlying sensitive data, thus improving accuracy while protecting privacy.
3Device complexity
If a unified model is used across all network units, then the system complexity is reduced, but the ability to handle diverse security events and personalized needs is compromised
Solution Approach 1:
The patent implements a universal multi-functional model architecture where the convolutional neural network, attention mechanism, and fully connected network work together to handle multiple types of security events across different network units. The unified structure processes diverse security event data (DDoS attacks, data breaches, malware infections, etc.) through the same hierarchical processing pipeline, achieving both structural simplicity and high adaptability to different security scenarios.
Solution Approach 2:
The patent applies local quality by allowing each network unit to customize its own instance of the unified model according to its specific security needs and event characteristics. While the overall architecture remains unified, each network unit can adjust parameters and focus areas based on its local context, maintaining both system simplicity and tailored adaptability to diverse security events at each location.
4Quantity of substance
If multiple monitoring points monitor various security events, then the quantity of security information increases, but the difficulty of determining overall security situation increases
Solution Approach 1:
The patent extracts and separates the complex task of determining overall network security situation into independent processing stages handled by specialized network components. The convolutional neural network extracts local security features from individual monitoring points, the attention mechanism extracts and weights the most important features across all monitoring points, and the fully connected network extracts the final security situation grade. This extraction and separation of functions transforms the difficult integrated measurement into a series of manageable processing steps.
Data Source
AI summary
A network security situation awareness method based on collaborative learning is provided. The method includes integrating network security situation data monitored at different monitoring points in network units, and determining a unified data presentation form in each of the network units; obtaining an initial network security situation awareness model by constructing and optimizing a local network security situation awareness process; improving generalization ability of the network unit in feature extraction to meet a preset condition by using a collaborative learning framework, and obtaining a final network security situation awareness model by performing secondary fine-tuning on the personalized customization component based on the attention mechanism; performing network security situation awareness on a target network unit by using the final network security situation awareness model, and updating a training set of the final network security situation awareness model according to a network security situation awareness result.