Automated Network Supervision for Anonymous Node Identification
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Solution Overview
Problem
Conventional methods for managing organizational networks require costly and inconvenient manual investigations to identify the owner or administrator of technology devices and software applications, especially in large-scale environments with thousands or hundreds of thousands of nodes.
Innovation Solution
An automated network supervision system that detects anonymously administered nodes, aggregates system log files, and uses unsupervised machine learning to identify administrators, generating registration records and updating network participant registries without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual investigation methods are used to identify network device owners and administrators, then identification accuracy can be maintained, but the process becomes costly and time-consuming
Solution Approach 1:
The patent replaces manual mechanical investigation processes with automated electronic data collection and analysis systems. The system automatically gathers device information, network traffic data, and user activity logs, then uses computational algorithms to identify owners and administrators, eliminating the need for manual field investigations while maintaining identification accuracy.
Solution Approach 2:
The system enables self-service identification by automatically collecting and analyzing data from network devices, log files, and user profiles. The automated system performs the identification function without requiring manual intervention, allowing the network management process to serve itself rather than relying on human investigators.
2Loss of information
If manual investigation processes are employed to identify technology device owners, then detailed information can be obtained, but the operational complexity and cost increase significantly
Solution Approach 1:
The patent creates a universal automated system that handles multiple identification tasks across different device types, network segments, and user roles through a single integrated platform. The system collects diverse data from various sources (network traffic, device logs, user profiles) and processes them through unified algorithms, eliminating the need for separate manual investigation procedures for each device or situation.
Solution Approach 2:
The system replaces complex manual investigation procedures with automated electronic data collection and analysis. The automated system gathers comprehensive information from multiple network sources simultaneously and uses computational methods to process this data, reducing process complexity while maintaining information completeness.
3Ease of operation
If pre-registration of network device owners is required, then network management can be simplified, but the system becomes less adaptable to dynamically added devices
Solution Approach 1:
The system performs preliminary automated identification by proactively collecting device information, analyzing network traffic patterns, and generating owner/administrator identifications before manual intervention is needed. This preliminary automated action maintains ease of operation while adapting to newly added devices without requiring pre-registration, as the system automatically processes and identifies devices as they join the network.
Solution Approach 2:
The patent implements a dynamic identification system that automatically adapts to changing network conditions and newly added devices. The system continuously monitors network traffic, collects device data, and updates identifications in real-time, allowing the network management process to remain simple while being highly adaptable to dynamic device additions without requiring pre-registration.
Data Source
AI summary
According to one implementation, a network supervision system includes one or more computing platform(s) coupled to multiple nodes of a network including the computing platform(s), the computing platform(s) including a hardware processor and a system memory storing a network participant supervising software code and a network participant registry. The hardware processor executes the network participant supervising software code to detect an anonymously administered node of the network, aggregate system log files of the anonymously administered node, and perform an analysis of the system log files using an unsupervised machine learning algorithm to identify an administrator of the anonymously administered node. The hardware processor further executes the network participant supervising software code to generate a registration record associating the administrator with the anonymously administered node, and update the network participant registry using the registration record.