Siamese Neural Network Screening for Unauthorized Resource Interactions
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Solution Overview
Problem
There is a need for a system to detect and prevent misappropriation attempts using Siamese Neural Networks, as bad actors are finding new ways to acquire resource credentials and perform unauthorized resource interactions.
Innovation Solution
A system utilizing Siamese Neural Networks is employed to monitor resource interactions, capture information, calculate execution-related values, extract patterns, and determine unauthorized interactions by training with historical data, applying filtering mechanisms, and flagging suspicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional filtering mechanisms are used to detect unauthorized interactions, then the system can identify suspicious activities, but the detection precision is insufficient against new misappropriation methods
Solution Approach 1:
The patent replaces traditional mechanical filtering mechanisms with a Siamese Neural Network system that uses deep learning to detect unauthorized interactions. The neural network processes execution values and behavior data to identify misappropriation attempts, achieving superior detection precision against new attack methods while maintaining manageable system complexity through automated learning.
2Measurement precision
If the system monitors all resource interactions in detail, then detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant features from resource interactions for analysis. The Siamese Neural Network focuses on computing execution values and comparing behavior patterns rather than analyzing all raw interaction data, thereby maintaining high detection accuracy while reducing processing time and computational overhead.
3Adaptability or versatility
If the filtering mechanism uses static rules, then the system is easier to implement, but it cannot adapt to new misappropriation techniques
Solution Approach 1:
The patent implements a dynamic filtering mechanism using a Siamese Neural Network that continuously learns from new data. The system adapts to emerging misappropriation techniques by training on new execution values and behavior patterns, maintaining high adaptability while the underlying neural network architecture provides a standardized implementation framework.
4Reliability
If more historical data is used for training the neural network, then detection reliability improves, but the training time and data processing requirements increase
Solution Approach 1:
The patent uses a sufficient subset of historical data for training the Siamese Neural Network rather than processing all available data. The system processes execution values and behavior patterns from historical unauthorized interactions to achieve reliable detection, balancing training comprehensiveness with acceptable training time through targeted data selection.
Data Source
AI summary
Embodiments of the present invention provide a system for performing misappropriation detection and prevention using Siamese Neural Networks. The system is configured for identifying initiation of a resource interaction by a user, via an interaction device, determining if the resource interaction meets criteria associated with an unauthorized interaction based on applying a filtering mechanism, determining that the resource interaction meets the criteria associated with the unauthorized interaction and flag the resource interaction, passing the resource interaction to a Siamese Neural Network to determine if the resource interaction is unauthorized, and approving or denying the resource interaction based on determining if the resource interaction is unauthorized.