Automated Root-Cause Analysis for Wi-Fi Authentication Failures
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Solution Overview
Problem
Current systems require significant human effort and time to identify and remediate Wi-Fi authentication failures in LAN and Wi-Fi systems, as they involve manual collection and correlation of syslog data to determine root-causes.
Innovation Solution
An analysis system that includes a network interface, a processing device, and a memory device configured to monitor Wi-Fi systems, detect authentication failures, analyze root-causes, and automatically remediate issues using a closed-loop automation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual collection and correlation of syslog data is performed to identify root-causes, then measurement precision of root-cause identification is improved, but loss of time and human effort increases
Solution Approach 1:
The patent replaces manual mechanical processes of collecting and correlating syslog data with an automated machine learning system. The ML model automatically ingests syslog data from multiple network devices, performs correlation analysis, and identifies root-causes without human intervention, thereby maintaining measurement precision while dramatically reducing time loss.
Solution Approach 2:
The patent introduces an intermediary ML-based analysis system that acts as a mediator between raw syslog data and root-cause identification. This intermediary automatically processes, correlates, and analyzes the data, eliminating the need for manual intervention while preserving the accuracy of root-cause determination.
2Reliability
If comprehensive syslog data from all network devices is collected and correlated, then reliability of root-cause determination is improved, but device complexity and human effort increase
Solution Approach 1:
The patent replaces complex manual data collection and correlation mechanisms with an automated ML system. The system automatically gathers syslog data from multiple network devices, performs sophisticated correlation analysis across different data sources, and determines root-causes with high reliability without requiring human operators to manage the complexity.
Solution Approach 2:
The patent creates a universal ML-based analysis platform that handles multiple functions: collecting syslog data from various network devices, correlating events across different sources, analyzing authentication failures, and identifying root-causes. This single multi-functional system replaces multiple separate manual processes, reducing overall complexity while maintaining reliability.
3Productivity
If automated remediation is implemented, then productivity is improved, but ease of operation decreases due to system complexity
Solution Approach 1:
The patent implements a self-service automated remediation system that automatically executes corrective actions based on ML-identified root-causes. The system autonomously performs remediation tasks such as reconfiguring network devices or triggering alerts without requiring human operators to manually intervene, thereby maximizing productivity while keeping the user interface simple.
Solution Approach 2:
The patent incorporates feedback mechanisms where the automated remediation system monitors the effects of applied corrections and uses this information to refine future remediation actions. This closed-loop feedback ensures that automated operations remain effective and adaptable, maintaining productivity while requiring minimal human oversight.
Data Source
AI summary
Systems and methods for analyzing root-causes of network access failures in a wireless network. In response to detecting that a client device experiences a network access failure that prevents communication with a server device, a method, according to one implementation, includes a step of analyzing the network access failure to predict one or more root-causes. Also, the method includes beginning a remediation procedure for remediating the one or more root-causes.