Remote LAN Incident Detection Using Severity and Criticality Scoring
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Solution Overview
Problem
Existing methods for diagnosing and resolving incidents in local area networks (LANs) are inefficient, leading to suboptimal response times and customer dissatisfaction, as they lack rapid remote incident detection and resolution capabilities.
Innovation Solution
An incident detection device connected via a wide area network that collects and aggregates descriptive data from LANs, calculates severity and criticality scores for anomalies, and generates recommendations or corrective actions based on these scores to quickly diagnose and resolve network incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If existing methods for diagnosing and resolving incidents in LANs are used, then the process can be completed, but the response time is suboptimal and customer satisfaction is reduced
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing network data before incidents occur. The incident detection device monitors network parameters, calculates severity scores, and identifies anomalies proactively, enabling early intervention before customers experience significant service degradation.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring network data, calculating severity scores, and comparing current states against historical patterns. This feedback loop enables the system to detect incidents rapidly, generate appropriate recommendations, and adjust to changing network conditions in real-time.
2Productivity
If manual incident diagnosis and resolution is performed, then technical support can address issues, but the process is inefficient and slows down resolution
Solution Approach 1:
The system enables self-service by automatically detecting incidents, calculating severity scores, and generating diagnostic recommendations without requiring immediate manual intervention. The incident detection device autonomously analyzes network data, identifies anomalies, and provides actionable insights that accelerate resolution while reducing dependency on manual technical support.
Solution Approach 2:
The system replaces manual mechanical processes with automated electronic analysis. Instead of technicians manually examining network data and diagnosing issues, the incident detection device automatically processes network parameters, calculates severity scores, and generates recommendations, significantly improving efficiency and reducing diagnosis time.
3Measurement precision
If comprehensive network data is collected and analyzed, then accurate incident detection is achieved, but the complexity of the detection system increases
Solution Approach 1:
The system applies segmentation by dividing network data collection and analysis into distinct modular components. The incident detection device separates data collection, validation, aggregation, severity score calculation, and recommendation generation into independent functional modules, making the complex system manageable and maintainable while preserving detection accuracy.
Solution Approach 2:
The system utilizes parameter changes by transforming raw network data into meaningful severity scores through standardized parameter transformations. The incident detection device converts diverse network parameters into unified severity metrics, enabling accurate anomaly detection while simplifying the analysis process through consistent parameter handling.
Data Source
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AI summary
The present invention relates to a method and device for detecting incidents in a local area network (LAN) by means of an incident detection device, the incident detection device being connected to the LAN via a wide area network (WAN), and a data collection agent collecting descriptive data from the LAN. The invention is capable of detecting different types of anomalies and: - receives (E50) messages from the collection agent, validates and aggregates data contained in each received message into data groups, - calculates (E51) a severity score for each data group, for each type of anomaly, and calculates a total severity score for each data group, - calculates (E53) a total criticality score on all the total severity scores, - generates (E54) messages of recommendations or corrective actions, at least based on the total criticality score.