Telecom Network Configuration Anomaly Detection Using Vector Similarity
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Solution Overview
Problem
Existing methods fail to detect anomalies in telecommunication network configurations, particularly in critical nodes, due to the high volume of changes and parameters involved, which makes using static methods impractical.
Innovation Solution
A method and apparatus for determining anomalies in telecommunication network configurations by converting the configuration into a query vector representation, retrieving similar historical configurations, and generating an anomaly warning if the difference exceeds a threshold, using NLP and graph embedding models to analyze the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static methods are used to monitor network configurations, then the monitoring process is simple, but the system cannot detect anomalies due to high volume of changes and parameters
Solution Approach 1:
The system transitions from static configuration monitoring to dynamic anomaly detection by continuously comparing current configurations against historical data and learned patterns. The monitoring approach adapts to changing network conditions while maintaining automated operation, resolving the contradiction between detection capability and system complexity.
Solution Approach 2:
An intermediary anomaly detection system is introduced between the network configuration and manual review processes. This intermediary automatically analyzes configuration changes, identifies anomalies, and only presents suspicious changes for human review, thereby improving reliability without proportionally increasing overall system complexity.
2Productivity
If manual review of all configuration changes is performed, then accuracy is high, but the process is time-consuming and impractical for high volume changes
Solution Approach 1:
Instead of reviewing all configuration changes manually (excessive action), the system applies automated anomaly detection to filter changes and only presents suspicious cases for manual review (partial action). This selective approach maintains high detection accuracy for critical anomalies while dramatically improving processing throughput for the high volume of routine changes.
Solution Approach 2:
The anomaly detection system performs self-service by automatically analyzing configuration changes, learning from historical data, and identifying anomalies without requiring manual intervention for every change. This automation handles the high volume of routine changes efficiently while maintaining accuracy through continuous learning and adaptation.
3Reliability
If comprehensive configuration monitoring is implemented, then detection capability improves, but the computational resources required increase
Solution Approach 1:
The system extracts and focuses only on the critical features and parameters relevant to anomaly detection from the comprehensive configuration data, rather than processing all configuration details equally. This selective extraction maintains high detection capability while reducing the computational resources required by concentrating processing effort on the most informative aspects of configuration changes.
Data Source
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AI summary
Provided is a method for determining an anomaly in a configuration of a telecommunication network, the method including converting the configuration of the telecommunication network into a corresponding query vector representation, retrieving one or more historical vector representations of historical configurations of the telecommunication network stored in a vector database that are most similar to the query vector representation, generating an anomaly warning if a difference between the one or more historical vector representations and the query vector representation exceeds a threshold.