Determining hierarchical information from an internet protocol address to predict an entity attribute
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Solution Overview
Problem
Conventional IP address attribute prediction systems, such as rule-based methods, are resource-intensive, inaccurate, and not scalable due to the fluid partitioning of subnetworks and changes in IP address versions, leading to inefficiencies in content distribution and potential privacy issues.
Innovation Solution
A neural network model, specifically a convolutional neural network (CNN), is trained to perform multiclass classification on hierarchical features extracted from IP addresses, predicting entity attributes like entity name and size using truncated IP addresses to enhance content distribution and anonymize user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based methods are used for IP address attribute prediction, then implementation is simple, but accuracy is low and resource consumption is high
Solution Approach 1:
The patent replaces rule-based mechanical systems with a neural network model that automatically learns hierarchical features from IP addresses. The neural network processes IP address components through multiple layers to predict entity attributes, eliminating the need for manual rule construction while achieving higher accuracy and lower resource consumption compared to conventional rule-based approaches
2Adaptability or versatility
If conventional IP address prediction systems are used, then implementation is straightforward, but scalability is poor due to fluid partitioning of subnetworks
Solution Approach 1:
The neural network model dynamically adapts to fluid partitioning of subnetworks by learning hierarchical representations of IP address components. The model can generalize across different network partitions and IP address versions without requiring explicit retraining, enabling scalable deployment while maintaining reliable predictions despite changing network topologies
Solution Approach 2:
The system handles parameter changes in IP address versions and subnetwork partitions by training the neural network on diverse hierarchical features. The model learns invariant representations that remain valid across different IP address formats and network configurations, ensuring reliable predictions even as network parameters evolve
3Measurement precision
If full IP address information is used for prediction, then prediction accuracy is high, but user privacy is compromised
Solution Approach 1:
The patent extracts only the necessary hierarchical features from IP addresses for prediction purposes. By focusing on higher-level network identifiers and aggregating data across multiple IP components, the system achieves accurate predictions without requiring or storing individual user IP addresses, thereby maintaining privacy while preserving predictive capability
Solution Approach 2:
The neural network acts as an intermediary that processes IP address information through hierarchical feature extraction. This intermediate representation layer allows the system to learn patterns from IP data without directly accessing or storing raw IP addresses, serving as a privacy-preserving mediator between data collection and prediction functions
Data Source
AI summary
Embodiments of the disclosed technologies are capable of predicting entity attributes using an Internet Protocol (IP) address. The embodiments describe obtaining an IP address. The embodiments further describe extracting routing prefixes from the IP address. The embodiments further describe performing multiclass classification using a convolutional neural network applied to the extracted routing prefixes to obtain an entity attribute. The embodiments further describe providing the entity attribute for mapping the entity attribute to digital content.