Smart Analyzer for Automated Network Anomaly Data Capture
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Solution Overview
Problem
In mobile and cloud computing environments, network administrators face difficulties in troubleshooting data network anomalies due to unpredictable user terminal locations and server allocations, making it challenging to detect and address usage anomalies effectively.
Innovation Solution
A network node equipped with a smart analyzer that establishes baseline statistics, detects anomalies, and captures network data using predefined rules, allowing for automated detection and recording of anomalies in network traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual troubleshooting methods are used by network administrators, then detailed examination of network data can be performed, but the process is time-consuming and inefficient due to unpredictable user terminal locations and server allocations in mobile and cloud computing environments
Solution Approach 1:
The system performs preliminary actions by establishing baseline usage patterns through baselining rules before anomalies occur. The smart analyzer continuously monitors network traffic and pre-processes data to detect deviations from normal behavior, enabling faster response to anomalies without waiting for manual intervention.
Solution Approach 2:
The troubleshooting system performs self-service through automated anomaly detection and data capture. The smart analyzer independently monitors network traffic, applies anomaly rules, detects deviations, and captures relevant network data without requiring manual network administrator intervention for each anomaly event.
2Loss of information
If comprehensive network data is captured during anomalies for detailed analysis, then the quantity and detail of network data increases, but the complexity of processing and analyzing this data increases
Solution Approach 1:
The smart analyzer extracts only the relevant network data needed for anomaly analysis by applying capturing rules that selectively capture data based on the detected anomaly type. This extraction approach obtains necessary information without capturing unnecessary data, reducing processing complexity while maintaining data completeness for troubleshooting.
Solution Approach 2:
The system applies different capturing rules with specific qualities tailored to different anomaly types. Each capturing rule is designed with local quality characteristics that capture only the specific data attributes relevant to particular anomaly patterns, optimizing data collection efficiency and reducing overall system complexity.
Data Source
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AI summary
Methods and systems are provided for automatically capturing network data for a detected anomaly. In some examples, a network node establishes a baseline usage by applying at least one baselining rule to network traffic to generate baseline statistics, detects an anomaly usage by applying at least one anomaly rule to network traffic and generating an anomaly event, and captures network data according to an anomaly event by triggering at least one capturing rule to be applied to network traffic when an associated anomaly event is generated.