Behavioral Profile Verification via Network Activity Segmentation
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Solution Overview
Problem
Current methods lack effective means to classify and verify identities based on complex behavioral patterns in internet activities, which are influenced by individual personalities and vary significantly across different online environments.
Innovation Solution
A system and method for profiling users by receiving and analyzing streams of network activities, classifying them into activity-specific categories, extracting attributes, calculating scores, and mapping these to individual profiles using machine learning techniques, creating dynamic profiles that update over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional identity verification methods are used, then the verification process is simple, but they cannot accurately verify identities based on complex behavioral patterns across different online activities
Solution Approach 1:
The system segments identity verification into multiple independent modules: behavioral attribute extraction module, profile database module, scoring module, and verification module. Each module handles specific aspects of the verification process, allowing complex behavioral analysis to be broken down into manageable components that can be processed independently and combined for final verification
Solution Approach 2:
The system transforms identity verification from static document-based validation to dynamic behavioral parameter analysis. It extracts and analyzes multiple behavioral attributes (typing patterns, navigation behavior, interaction timing, language usage) and converts them into quantitative scores that dynamically represent user identity and risk levels
2Measurement precision
If behavioral attributes from multiple network activities are analyzed, then identity verification accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing behavioral data in the background during normal user activities. Behavioral attributes are extracted and stored in profile databases before verification is needed, so when verification occurs, the system can quickly retrieve and compare pre-analyzed behavioral patterns rather than processing raw data from scratch
Solution Approach 2:
The system replaces traditional mechanical identity verification (manual document checking) with automated electronic behavioral analysis. Machine learning algorithms and automated scoring systems substitute for manual verification processes, enabling rapid processing of multiple behavioral attributes across different network activities without proportional increases in processing time
3Measurement precision
If comprehensive behavioral attributes are monitored across different internet activities, then user profiling accuracy improves, but the complexity of data collection and analysis increases
Solution Approach 1:
The system implements a universal behavioral attribute extraction framework that works across multiple internet activities (email, chat, browsing, file transfers). The same core extraction mechanisms and scoring methodologies are applied universally to different activity types, standardizing data collection and analysis processes while maintaining the ability to capture activity-specific behavioral nuances
Solution Approach 2:
The system introduces behavioral attribute databases and scoring functions as intermediary layers between raw network activity data and identity verification results. These intermediaries aggregate, normalize, and structure diverse behavioral data from multiple sources, making the data more manageable and easier to analyze while preserving comprehensive behavioral information
Data Source
AI summary
Systems, methods and computer products for profile-based identity verification over the Internet. Exemplary embodiments include a method for profiling a user on a network, the method including receiving an input of streams corresponding to network activities associated with the user, wherein the input of streams are received from one or more layers of the network, in response to receiving a request to supply specified-input, receiving a score function and a list of attributes to be monitored, classifying the input of streams into network-activity classifications, extracting values and attributes for the network-activity classifications, and placing the extracted values and attributes into data sets, calculating a score of the data sets, comparing the data sets to a database of activity-specific attributes and mapping the data sets to a class of individuals based on a value of the score and the comparison of the database of activity-specific attributes.