Network Anomaly Detection Using TB-CNN Configuration Trees
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Solution Overview
Problem
Existing network management systems struggle to efficiently detect and remediate anomalous behaviors, leading to costly downtime, system errors, and security threats due to cyber-attacks, malware, and configuration changes.
Innovation Solution
Employing a machine learning-based anomaly prediction tool using a tree-based convolutional neural network (TB-CNN) to analyze network configuration data, identify anomalous behaviors, and autonomously correct them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network monitoring systems are used to detect anomalous behaviors, then system reliability is maintained through manual monitoring, but detection precision and response time deteriorate leading to costly downtime and security threats
Solution Approach 1:
The patent replaces manual network monitoring mechanisms with an automated machine learning system. The TB-CNN model automatically analyzes network configuration data, generates data trees, and detects anomalies without human intervention, thereby improving detection precision while reducing downtime associated with manual monitoring and response.
Solution Approach 2:
The system performs preliminary anomaly detection by continuously analyzing network configuration data and generating data trees before actual security incidents or system failures occur. The ML model predicts potential anomalies in advance, allowing proactive remediation actions to be taken before downtime occurs.
2Productivity
If manual monitoring and remediation processes are used, then system complexity is kept simple through straightforward procedures, but productivity deteriorates due to slow response times and high operational costs
Solution Approach 1:
The patent implements self-service through automated anomaly detection and remediation. The system autonomously monitors network configurations, detects anomalies using the TB-CNN model, and triggers remediation actions without requiring human operators. This automation dramatically improves remediation speed while the modular architecture manages system complexity through organized data processing pipelines.
Solution Approach 2:
The system transforms network configuration data into structured data trees with specific parameters and formats suitable for ML analysis. By changing the representation parameters of network data into a standardized tree structure, the system enables automated processing while managing complexity through consistent data transformation rules.
3Reliability
If comprehensive network monitoring is implemented to detect all anomalous behaviors, then detection coverage is improved, but system complexity and computational resources worsen
Solution Approach 1:
The patent segments the network monitoring task into distinct components: data collection, data tree generation, anomaly detection using TB-CNN, and remediation. This segmentation allows comprehensive monitoring coverage while managing complexity by dividing the system into specialized modules, each handling a specific aspect of the monitoring process.
Solution Approach 2:
The data tree structure serves as an intermediary between raw network configuration data and the ML model analysis. This intermediate representation standardizes diverse network data into a unified format, enabling comprehensive monitoring without directly increasing the complexity of the core detection algorithms.
Data Source
AI summary
An anomaly prediction tool is employed on a communication network to monitor systems and/or devices on the network for anomalous behaviors. The anomaly prediction tool can include a machine learning (ML) model to detect anomalous network systems operating on the network based on a data tree derived from network configuration data for the network. In some examples, the ML model is a graph-based convolutional neural network.