Neural Network Model for Predicting Computer Network Attack Vulnerabilities
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Solution Overview
Problem
Current methods for predicting attack vulnerabilities in computer networks are inefficient and resource-intensive, as they rely on manual graph construction and lack real-time predictive capabilities.
Innovation Solution
A neural network model is trained using preprocessed topology and asset information to predict attack vulnerabilities by generating a host connection matrix, asset connection information, and attack path information, allowing for quick inference and visualization of potential threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual graph construction methods are used for vulnerability prediction, then measurement precision can be achieved, but productivity is reduced and resource consumption increases
Solution Approach 1:
The patent replaces manual graph construction and traditional vulnerability scanning methods with a neural network-based machine learning system. The neural network automatically learns attack patterns and predicts vulnerabilities without requiring manual graph construction, thereby maintaining prediction accuracy while dramatically improving speed and reducing resource consumption.
Solution Approach 2:
The patent creates a virtual copy of the target network environment for training the neural network model. By preprocessing topology and asset information into training datasets in this virtual environment, the system can learn attack patterns without affecting the actual production system, enabling rapid prediction deployment.
2Reliability
If traditional vulnerability scanning tools are used, then vulnerability detection capability is provided, but resource consumption increases and real-time prediction is not achieved
Solution Approach 1:
The patent performs preliminary actions by pre-processing network topology and asset information into structured training datasets before deploying the neural network. This preprocessing step, including creating host connection matrices and attack path tables, enables the model to make rapid predictions without requiring intensive resources during actual vulnerability scanning operations.
Solution Approach 2:
The neural network model becomes self-sufficient after training, automatically predicting vulnerabilities without requiring continuous human intervention or heavy computational resources. The model serves itself by making rapid predictions based on learned patterns, reducing ongoing resource consumption while maintaining reliable detection capability.
3Measurement precision
If comprehensive topology and asset information is collected for accurate prediction, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the complex vulnerability prediction task into distinct components: topology information collection, asset information collection, data preprocessing, model training, and prediction inference. By dividing the system into these modular components, each handling a specific aspect of the data, the overall system complexity is managed while maintaining comprehensive data collection for accurate predictions.
Solution Approach 2:
The patent introduces an intermediary data preprocessing layer that transforms raw topology and asset information into structured training datasets. This intermediary step, which includes creating host connection matrices and attack path tables, simplifies the data format and makes it more suitable for neural network processing, thereby reducing system complexity while preserving prediction accuracy.
Data Source
AI summary
Collecting the topology and asset information of the virtual generated computer network, converting the topology and asset information into a training data set for training the neural network model, training the neural network model based on the training data set, and training A method and apparatus for predicting an attack vulnerability of a computer network through the step of inferring an attack vulnerability of a target computer network using a neural network model are provided.