Cloud Server Detecting LAN Update Events via Similarity Parameters
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Solution Overview
Problem
Software updates on connected devices often disrupt functionality, making it difficult for technical support to diagnose and authenticate update events, leading to user experience issues and increased service calls, as existing technologies lack efficient methods to detect and monitor update events across multiple devices within a network.
Innovation Solution
A system and method that form a communications network of local area networks based on similarity parameters to detect and authenticate update events by calculating an update event likelihood score, using a cloud server to identify and notify devices when the score exceeds a threshold, thereby minimizing disruption and assisting in troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software updates are implemented on connected devices, then software functionality and security are improved, but device compatibility and operational stability deteriorate
Solution Approach 1:
The system performs preliminary detection of update events by monitoring download files before they are fully installed. By detecting the download phase and analyzing file characteristics, the system can prepare diagnostic information and notify users before the update actually modifies device functionality, allowing for preventive measures to be taken if compatibility issues are anticipated.
2Reliability
If software updates are deployed across multiple devices, then overall system security is improved, but diagnostic capability and update authentication deteriorate
Solution Approach 1:
The system merges data from multiple sources including download file characteristics, device information, network traffic patterns, and update deployment metadata into a unified diagnostic framework. This consolidated approach enables comprehensive update detection and authentication across distributed devices by correlating information that would be insufficient when examined in isolation.
Solution Approach 2:
The system implements feedback mechanisms where detected update events are reported back to the cloud server, which then provides authentication verification and diagnostic information. This closed-loop feedback enables continuous monitoring and verification of update deployments across multiple devices, allowing the system to adapt its detection strategies based on observed update patterns and potential threats.
3Measurement precision
If update monitoring is implemented across all devices, then update authentication is improved, but network complexity and computational resources increase
Solution Approach 1:
The system introduces a cloud server as an intermediary that centralizes the complex tasks of update monitoring, analysis, and authentication. Individual devices only need to report basic download event information to the cloud server, which then performs the computationally intensive analysis and coordination. This intermediary approach enables precise update authentication across the network without requiring each device to implement complex monitoring infrastructure.
Data Source
AI summary
A method comprising: identifying, by a cloud server, a set of local area networks (LAN) associated with the cloud server, based on a similarity parameter with respect to an end device connected within each of the LANs; forming a communications network comprising all of the LANs in the set; detecting, by at least a subset of the LANs, a download file received by the respective end devices in each of the LANs in the subset; calculating an update event likelihood score with respect to the download file, based, at least in part, on a plurality of parameters associated with the download file; and issuing, by at least one of the LANs in the subset, a notification that the download file is associated with an update event affecting all of the end devices in each of the LANs, when the update event likelihood score exceeds a specified threshold.