Network Anomaly Detection Pipeline for Real-Time Fault Classification
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Solution Overview
Problem
Wireless network operators face inefficiencies in detecting and classifying anomalies due to outdated methodologies, such as relying on static thresholds and human inspection, which can lead to delayed identification of potential problems affecting user experience.
Innovation Solution
A system architecture that employs machine learning models for real-time anomaly detection and classification, utilizing an online computing node to process network data, detect anomalies, and update models periodically through an offline storage and training function, allowing for proactive identification and autonomous issue resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are deployed for real-time anomaly detection, then detection accuracy and speed are improved, but device complexity and computational resource requirements increase
Solution Approach 1:
The system is divided into distinct functional modules: data collection module, preprocessing module, anomaly detection module using machine learning models, and classification module. Each module performs a specific task in the anomaly detection pipeline, making the complex system manageable and maintainable while achieving high detection accuracy through specialized processing at each stage
Solution Approach 2:
A preprocessing module serves as an intermediary between raw data collection and the machine learning anomaly detection models. This intermediary layer prepares and transforms data into suitable formats for ML processing, reducing the computational burden on the detection models while maintaining high detection accuracy through proper data conditioning
2Reliability
If more network parameters are monitored to improve anomaly detection coverage, then detection completeness is improved, but data processing time and human resources increase
Solution Approach 1:
Manual visual inspection by network monitor teams is replaced with automated machine learning-based anomaly detection systems. The ML models automatically analyze multiple network parameters simultaneously, providing comprehensive anomaly detection coverage across numerous KPIs without requiring human time investment for data collection and inspection
Solution Approach 2:
The system performs self-monitoring and self-diagnosis by automatically collecting, processing, and analyzing network performance data. The anomaly detection and classification functions operate autonomously without requiring human intervention for routine monitoring tasks, enabling the system to handle extensive parameter monitoring efficiently
3Ease of manufacture
If static thresholds are used for anomaly detection, then implementation simplicity is maintained, but detection capability for subtle anomalies deteriorates
Solution Approach 1:
The system transitions from fixed static threshold parameters to dynamic, adaptive parameters learned by machine learning models from historical network data. The ML models automatically adjust detection parameters based on learned patterns and anomalies in the data, significantly improving detection sensitivity for subtle anomalies while maintaining ease of implementation through automated parameter optimization
Data Source
AI summary
Systems and methods are disclosed for detecting and classifying faults in a networked system. An offline training offline storage and training function can store network data and periodically train machine learning models for identifying faults. Machine learning models can include separate detection and classification models. These machine learning models can be periodically provided to an online anomaly detection function and an online anomaly classification function for detecting and classifying anomalies in real time.