Co-Controlled Network Activity Detection for Fraud Permission Control
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Solution Overview
Problem
Current systems are incapable of adequately detecting co-controlled operations within network environments, which can lead to fraudulent activities such as payment fraud, return abuse, and fake reviews, as they assume unrelated network users and fail to identify related actors engaging in such behaviors.
Innovation Solution
A system utilizing a trained fraud detection model to analyze network activity datasets, identify co-controlled systems, and modify their permissions to prevent fraudulent activities, employing machine learning techniques like neural networks and decision trees to predict co-controlled interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current systems assume unrelated network users and operate without co-control detection, then network operations can proceed with simple assumptions and basic operations, but fraudulent activities such as payment fraud, return abuse, and fake reviews cannot be detected
Solution Approach 1:
The system performs preliminary actions by training the fraud detection model on historical network activity data before actual fraud detection occurs. The model learns patterns of co-controlled operations in advance, enabling it to identify fraudulent activities when they occur without requiring complex real-time analysis of all network interactions.
Solution Approach 2:
The trained fraud detection model serves as an intermediary between raw network activity data and fraud determination. The model translates complex network interaction patterns into interpretable fraud scores, acting as a mediator that simplifies the detection process while maintaining high accuracy in identifying co-controlled operations.
2Measurement precision
If a trained fraud detection model is implemented to identify co-controlled systems, then fraudulent activities can be detected with high accuracy, but system complexity increases due to model training and deployment requirements
Solution Approach 1:
The fraud detection model performs self-service by automatically learning from historical data and continuously improving its detection capabilities. The system trains the model on its own historical network activity data, eliminating the need for external expert intervention in model development and reducing implementation complexity.
Solution Approach 2:
The system applies parameter changes by using different threshold values for fraud determination based on various factors such as activity type, time period, and risk level. This allows the model to maintain high detection accuracy across diverse fraud scenarios without requiring separate models for each fraud type, thereby reducing overall system complexity.
3Object-affected harmful factors
If permissions are modified in response to identifying co-controlled systems, then fraudulent operations can be prevented, but system operations may be disrupted by permission changes
Solution Approach 1:
The system applies partial action by modifying permissions selectively only for systems identified as co-controlled, rather than applying blanket restrictions to all systems. This targeted approach prevents fraudulent operations while minimizing disruption to legitimate system operations, maintaining ease of operation for unaffected systems.
Solution Approach 2:
The system implements feedback by continuously monitoring network activity and adjusting permissions based on real-time fraud detection results. When co-controlled systems are identified, permissions are modified and the system continues to monitor for further fraudulent activity, creating a closed-loop control mechanism that adapts to emerging threats while maintaining operational smoothness.
Data Source
AI summary
Systems and methods of generating fraud detection models and controlling network permissions of one or more systems within a network environment are disclosed. A network activity dataset comprising data representative of network activity within a network environment is received and at least one co-controlled system in the network activity dataset is identified by implementing a trained fraud detection model configured to receive the network activity dataset and output a fraud determination for each system having at least a first role in the network activity data. The fraud determination represents a likelihood of a system having the first role engaging in a co-controlled network activity. In response to identifying the at least one co-controlled system, one or more permissions of the at least one co-controlled system for operating within the network environment are modified.