User Identification via Analytical Module and Cookie Modification
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Solution Overview
Problem
Financial technology systems, such as banking, are vulnerable to cyber attacks due to unreliable and biased user information, leading to ineffective verification processes and access control.
Innovation Solution
A method and system for user identification based on the client device's environment, involving an analytical module that installs an iframe container in a web application to retrieve and modify cookie files, generating an in-use cookie file for global identification, and analyzing user data including digital fingerprints, network features, and behavioral habits to detect compromised devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional user identification methods are used based on provided client information, then the verification process is simple and fast, but the reliability and unbiasedness of user identification deteriorates due to unreliable and biased information
Solution Approach 1:
The system performs preliminary analysis of user environment data (device specifications, browser settings, installed applications, network configuration) before providing access to financial services. This advance verification establishes a baseline user profile that can be compared against subsequent requests, enabling reliable identification without adding complex real-time verification steps.
Solution Approach 2:
The verification process is divided into multiple independent modules: device information collection, browser environment analysis, network configuration checking, and behavioral pattern recognition. Each module independently evaluates specific aspects of the user environment, and their results are combined to form a comprehensive reliability assessment, making the complex verification process manageable and systematic.
2Measurement precision
If comprehensive user environment data is collected for reliable identification, then the accuracy of detecting compromised devices improves, but the amount of data processing and system complexity increases
Solution Approach 1:
The system extracts only the most critical and discriminatory features from the comprehensive user environment data, such as unique device identifiers, specific browser configuration patterns, network topology characteristics, and behavioral metrics. By focusing on these key indicators rather than processing all raw data, the system achieves high detection accuracy while minimizing processing complexity.
Solution Approach 2:
The system transforms raw user environment data into standardized parameters and features that are easier to process and compare. For example, converting device specifications into categorical features, transforming network configuration data into topology patterns, and normalizing behavioral data into metric scores. This parameter transformation simplifies subsequent analysis while preserving detection accuracy.
3Speed
If real-time analysis of user environment is performed to identify compromised devices, then the security response time improves, but the computational resources and processing time required increase
Solution Approach 1:
The system performs partial real-time analysis by continuously monitoring only the most critical security indicators and comparing them against baseline profiles. Instead of re-analyzing all user environment data with each request, the system focuses on detecting changes in key parameters and behavioral patterns, achieving fast security response with reduced computational overhead.
Solution Approach 2:
The system creates simplified copies or representations of user environment profiles that can be quickly compared against security thresholds. These condensed profile representations contain only the essential identification features and behavioral metrics needed for rapid security assessment, enabling fast decision-making without processing the full complexity of the original environment data.
Data Source
AI summary
A method and a system for user identification of a user based on a current user environment of a user device thereof are provided. The method comprises: causing the user device to receive an analytical module for installation thereof in a web application, the analytical module being configured to execute: causing installation of an iframe container in a given page of the web application launched on the user device; retrieving at least one cookie file including user data indicative of the current user environment of the user device; transmitting the at least one cookie file to an identification server for modification thereof to generate an in-use cookie file; and receiving the in-use cookie file; analyzing the in-use cookie file for identifying, based on the user data, the user device, and in response to identifying the user device as being compromised, transmitting a predetermined notification to the application content server.