Wireless Network Anomaly Detection With Adaptive Clustering
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Solution Overview
Problem
Current wireless communication systems face challenges in accurately and adaptively detecting abnormal performance indicators due to limitations in manual analysis, incomplete empirical knowledge bases, and difficulty in setting algorithm configuration parameters, leading to poor detection quality.
Innovation Solution
An anomaly detection method that generates clustering sets based on configuration and performance indicator data, determines algorithm configuration parameters for each set, and uses a preset anomaly detection algorithm to identify abnormal objects, incorporating operation state data for enhanced accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to detect abnormal performance indicator data, then the detection process is simple to implement, but the detection accuracy and self-adaptability are poor
Solution Approach 1:
The anomaly detection system automatically determines algorithm configuration parameters by self-training using historical performance indicator data and operation state data, without requiring manual parameter setting. The system performs self-adaptation through automated model training and parameter optimization, resolving the contradiction between high detection accuracy and system complexity by making the system self-configuring.
Solution Approach 2:
The system performs preliminary training of the anomaly detection algorithm using historical performance indicator data and operation state data before actual anomaly detection. Configuration parameters are determined in advance through self-training processes, allowing the system to be prepared for accurate detection without manual intervention during operation.
2Adaptability or versatility
If algorithm configuration parameters are manually set, then the implementation is straightforward, but the adaptability to different scenarios is poor
Solution Approach 1:
The system automatically determines algorithm configuration parameters through self-training using historical operation state data and performance indicator data. Different parameter sets are automatically generated for different clustering sets of network objects, providing scenario-specific adaptability without manual configuration effort.
Solution Approach 2:
Different algorithm configuration parameters are determined for different clustering sets of network objects based on their specific characteristics. Each clustering set receives customized parameters tailored to its local features, improving adaptability to different scenarios while maintaining ease of operation through automated differentiation.
3Measurement precision
If only runtime data is used for anomaly detection, then the data collection is simple, but the detection quality is poor
Solution Approach 1:
The system performs preliminary training using historical performance indicator data and operation state data before actual anomaly detection. This preliminary action enriches the detection model with patterns from accumulated historical data, improving detection quality beyond what runtime data alone can provide.
Solution Approach 2:
The system combines multiple data sources including performance indicator data, operation state data, and historical runtime data into a comprehensive training dataset. By merging these diverse data sources, the system achieves higher detection quality while systematically managing the total data volume required.
Data Source
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AI summary
An anomaly detection method and device, a terminal and a storage medium are disclosed. The method may include: generating at least one clustering set of objects based on configuration data and performance indicator data of the objects (210); determining an algorithm configuration parameter corresponding to each clustering set based on a preset anomaly detection algorithm and the performance indicator data corresponding to the objects in the clustering set (220); and determining, based on the algorithm configuration parameter, abnormal performance indicator data of the objects in the corresponding clustering set, so as to determine abnormal objects based on the abnormal performance indicator data (230).