Wireless Signaling Pattern Recognition for Identity Theft Detection
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Solution Overview
Problem
Current signaling protocols in telecommunications networks are complex and lack effective mechanisms to detect fraudulent behavior and identity theft, particularly in mobile and cellular-based systems, where unique identifiers like MDNs are vulnerable to misuse.
Innovation Solution
A system that analyzes patterns in wireless device location data derived from signaling networks, using statistical models to identify irregular behavior and detect potential identity theft by correlating location data with historical patterns and usage, employing modules like Event Processing, Wireless Device Location, and Location Recognition Logic to generate pattern values indicating fraudulent activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If signaling protocols are made more complex to provide sophisticated services, then service functionality is improved, but vulnerability to fraud and identity theft increases
Solution Approach 1:
The system performs preliminary analysis of signaling message patterns and device behavior before fraudulent activities can occur. By establishing baseline patterns of normal device behavior and pre-configuring detection rules, the system can identify and prevent fraud in advance, rather than reacting after the fact.
Solution Approach 2:
The patent introduces an intermediary fraud detection system that sits between the signaling protocols and the network infrastructure. This intermediary layer analyzes signaling messages without disrupting normal communication, extracting behavioral patterns and identifying anomalies that indicate fraudulent activity.
2Reliability
If location data is collected and analyzed for fraud detection, then identity protection is improved, but system complexity increases
Solution Approach 1:
The system uses multi-functional analysis modules that can process multiple types of signaling messages (location updates, handoff requests, authentication messages) through a unified pattern recognition framework. This universal approach reduces complexity by avoiding separate specialized systems for each message type.
Solution Approach 2:
The fraud detection system performs self-learning by automatically analyzing signaling patterns and adapting its detection algorithms based on observed behavior. The system autonomously updates its understanding of normal versus anomalous patterns without requiring manual configuration or intervention, reducing operational complexity.
Data Source
AI summary
A method and server computer for recognizing patterns in wireless device locations using wireless device location data derived from a communications signaling network is provided. By using wireless network supported signaling operations and messages, data including information regarding the subscriber, the subscriber's device, and/or the subscriber's service may be statistically analyzed to reveal potential patterns that may indicate meaningful behaviors of the purported users of those devices.