Neural Network VoIP Fraud Detection Using User Context
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Solution Overview
Problem
Current fraud detection systems in VoIP networks rely on rule-based methods that are inefficient, require labeled data, and do not consider user context, leading to delayed detection of fraudulent activities, which are adaptive and costly, compromising user privacy and security.
Innovation Solution
A computer-implemented method using machine learning to process call data and user features, generating vectorized embeddings for anomaly detection, employing neural networks to assess the likelihood of fraudulent calls in real-time, incorporating external data sources for attacker insights and updating user profiles for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based fraud detection systems are used, then expert knowledge can be applied to detect fraudulent behavior, but detection is delayed until after calls have ended and requires labeled data
Solution Approach 1:
The system performs preliminary actions by analyzing call data in real-time during the call, rather than waiting for the call to end. The neural network processes call features and user features concurrently as the call progresses, enabling fraud detection before the call completes and allowing immediate blocking of fraudulent calls.
Solution Approach 2:
The patent replaces the mechanical rule-based system with a neural network-based machine learning system. Instead of relying on predefined rules and labeled data, the neural network learns patterns from call data and user behavior features, enabling automated real-time fraud detection without requiring explicit labeling of fraudulent calls.
2Reliability
If rule-based systems rely on labeled data and expert knowledge, then fraud detection can be performed, but the system cannot consider user context and behavioral patterns
Solution Approach 1:
The system merges call data with user-specific behavioral features by combining call features (caller ID, callee ID, call duration, call time) with user features (historical calling patterns, device information, location data) into a unified neural network input, enabling comprehensive fraud detection that considers both call characteristics and user context.
Solution Approach 2:
The patent adds another dimension to fraud detection by incorporating user behavioral patterns and historical data alongside call-specific features. This multi-dimensional approach allows the neural network to analyze fraud risk from multiple perspectives simultaneously, considering both the call itself and the user's typical behavior patterns.
3Productivity
If automated machine learning-based fraud detection is implemented, then real-time fraud detection is enabled, but system complexity increases
Solution Approach 1:
The system segments the fraud detection process into distinct components: call feature extraction, user feature extraction, neural network processing, and decision-making. Each component handles a specific aspect of the analysis, making the overall complex system more manageable and maintainable while enabling real-time processing through specialized sub-systems.
Data Source
AI summary
A system for anomaly estimation for a telephonic call is described, receiving a call object including an identifier field associating the call object to a purported user; retrieving a user data object associated with the purported user, and processing the user data object to retrieve one or more vectorized user features associated with the purported user. A neural network processes the one or more vectorized call features and the one or more vectorized user features through a machine learning model.