Automated Network Device Classification for Protection Groups
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Solution Overview
Problem
Administrators face inefficiencies and inaccuracies in assigning protected devices to protection groups due to the cumbersome process of manual configuration and the lack of real-time adaptation to network changes, leading to reduced effectiveness and increased false alerts.
Innovation Solution
An interactive method using machine learning to classify IP addresses into protection groups, allowing for dynamic assignment based on observed network behavior, with user feedback to refine and automate the process, ensuring optimal protection settings are applied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If administrators manually assign protected devices to protection groups, then the assignment process is simple and direct, but it is time consuming and tedious
Solution Approach 1:
The system performs automated classification of protected devices into protection groups using machine learning algorithms. The classifier automatically analyzes device attributes, network traffic patterns, and behavioral characteristics to assign devices to appropriate protection groups without requiring manual administrator intervention, thereby eliminating the time-consuming manual assignment process while maintaining operational simplicity.
Solution Approach 2:
The patent replaces the manual mechanical process of device assignment with an automated computational system. Machine learning models and classification algorithms substitute for human administrators in the device assignment task, processing device data and making classification decisions automatically, thus converting a manual operational task into an automated intelligent system.
2Ease of operation
If administrators create a small number of protection groups to avoid tedious assignment, then the assignment process is simplified, but the granularity of protection settings is reduced
Solution Approach 1:
The automated classification system handles the complexity of creating and managing numerous granular protection groups without requiring administrator intervention. The machine learning classifier automatically determines the appropriate level of granularity and creates protection groups based on device characteristics, enabling fine-grained protection while maintaining operational simplicity for administrators.
Solution Approach 2:
The system dynamically adjusts the granularity of protection groups based on device behavior and network conditions. Rather than using a fixed number of protection groups, the automated classifier adapts the level of detail and number of groups according to the specific needs identified through machine learning analysis, optimizing both operational ease and protection granularity.
3Reliability
If protection group assignments are updated frequently to adapt to network changes, then the protection effectiveness is improved, but the system complexity increases
Solution Approach 1:
The system implements continuous monitoring of network traffic and device behavior, using this feedback to automatically update protection group assignments. The machine learning classifier receives ongoing data about device characteristics and network conditions, processes this feedback, and adjusts classifications accordingly, maintaining protection effectiveness through adaptive updates without requiring complex manual reconfiguration.
Solution Approach 2:
The automated classification system performs self-updating of protection group assignments based on changing network conditions and device behavior. The machine learning models continuously learn from new data and automatically reclassify devices when necessary, enabling the system to adapt to changes without increasing operational complexity or requiring manual intervention.
4Ease of operation
If manual device assignment is used, then the configuration process is straightforward, but false positive alerts and dropped legitimate traffic increase
Solution Approach 1:
The patent replaces manual configuration with automated machine learning-based classification to improve measurement precision in determining appropriate protection groups. The system analyzes multiple device attributes, network traffic patterns, and behavioral characteristics using computational algorithms, achieving more accurate classification decisions than manual methods while maintaining straightforward operation through automation.
Solution Approach 2:
The automated classification system independently analyzes device characteristics and determines optimal protection group assignments without human intervention. This self-service approach uses machine learning to evaluate device behavior and network patterns, achieving high classification accuracy that reduces false positives and protects legitimate traffic while keeping the configuration process simple for administrators.
Data Source
AI summary
A method and system for aggregating into a unique aggregated group (AG), protection groups (PGs) that are possible classifications with at least a threshold probability for a same unique combination of IP addresses. The PGs and the unique combination of IP addresses are included in the AG. Each of the IP addresses of the unique combination of IP addresses have respective associated probabilities for each PG included in the AG. The method further includes selecting and providing for display AGs based on the probabilities associated with the respective IP addresses included in the AGs, and providing for display at least one interactive graphical element in association with each AG selected for display. User activation of one of the interactive graphical element accepts assignment of one or more selected IP addresses included in the AG to a selected one of the one or more PGs included in the AG.