Network Confidence Score for Remote Transaction Security
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Solution Overview
Problem
Current methods for verifying the authenticity of a remote user's network during transactions are inadequate, as external IP addresses, BSSIDs, and SSIDs can be easily spoofed or obscured using VPNs, leading to potential fraudulent activities.
Innovation Solution
A system that generates a network confidence score based on network identification data and historical local network footprints, using device data, location data, and extended network data to determine the likelihood that a remote transaction is originating from a genuine local network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If external IP addresses, BSSIDs, and SSIDs are used to verify network authenticity, then the verification process is simple and quick, but the reliability of authentication is insufficient due to easy spoofing and VPN obfuscation
Solution Approach 1:
The patent segments the network verification process into multiple independent components: device hardware identification (device ID, IMEI), network identification (BSSID, SSID, IP address), location data (GPS coordinates), and transaction context analysis. Each segment provides a separate verification layer, making spoofing significantly more difficult while maintaining operational efficiency through parallel processing of these segments.
Solution Approach 2:
The patent implements nested verification where multiple identification layers are embedded within each other. The device ID is nested within the device profile, which is nested within the network context, which is nested within the transaction verification. This nested structure allows comprehensive authentication while presenting a unified verification process to users.
2Measurement precision
If multiple verification parameters (device data, location data, extended network data) are collected and analyzed, then the network confidence score accuracy is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The patent creates a universal verification system where a single network confidence score calculation mechanism handles multiple verification parameters. The same computational framework processes device data, location data, network data, and transaction context simultaneously, reducing the need for separate complex systems for each parameter type while maintaining high accuracy.
Solution Approach 2:
The patent transforms multiple complex verification parameters into a standardized confidence score parameter. By converting diverse data types (device identifiers, location coordinates, network signals) into a unified numerical confidence metric, the system simplifies processing and decision-making while preserving the precision benefits of multi-parameter analysis.
3Reliability
If a network confidence score system is implemented to assess local network authenticity, then fraudulent transaction risk is reduced, but the transaction processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary verification by pre-establishing device profiles and network footprints before transactions occur. Device characteristics, typical network environments, and location patterns are captured and stored in advance, allowing the confidence score system to quickly compare transaction data against pre-analyzed baselines rather than performing full analysis during each transaction, thus reducing processing time while maintaining fraud detection effectiveness.
Data Source
AI summary
A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method for providing network security. The system may receive customer credentials in association with an attempted transaction initiated by a user device that is connected to a local network. The system may receive network identification data associated with the local network and generate a network confidence score based on the network identification data and a historical local network footprint. The system may determine a security action based on the network confidence score.