Machine Learning Model for Synthetic User Account Detection
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Solution Overview
Problem
Conventional systems for detecting synthetic user accounts in digital systems are inflexible and inaccurate, relying on rigid rules that fail to adapt to new patterns associated with synthetic accounts.
Innovation Solution
The use of a machine learning model, such as a random forest classifier, to analyze features associated with user accounts and determine if they are synthetic, allowing for flexible adaptation to new patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rigid rules are used for detecting synthetic user accounts, then the detection system is simple and easy to implement, but the detection accuracy and adaptability to new patterns deteriorate
Solution Approach 1:
The patent replaces conventional rigid mechanical detection rules with a machine learning-based system. The machine learning model learns patterns from training data and makes predictions about synthetic user accounts, substituting the mechanical rule-based approach with an intelligent system that can adapt to new patterns without requiring manual rule updates.
Solution Approach 2:
The system changes the detection parameters from fixed rigid rules to dynamic machine learning models that can adapt to new patterns. The machine learning model continuously learns from data and adjusts its internal parameters to improve detection accuracy, allowing the system to respond to evolving synthetic account patterns effectively.
2Adaptability or versatility
If conventional rigid rules are used for detecting synthetic user accounts, then the system is easy to operate, but the adaptability to new patterns deteriorates
Solution Approach 1:
The machine learning system performs self-service by automatically learning from training data and improving its detection capabilities without human intervention. The system autonomously adapts to new patterns by processing data and updating its internal models, eliminating the need for manual rule creation and adjustment while maintaining ease of operation.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from detection results and adjusts its parameters accordingly. This feedback loop enables the system to adapt to new patterns and improve its performance over time while maintaining user-friendly operation through automated processes.
3Measurement precision
If machine learning models are used to detect synthetic user accounts, then the detection accuracy and flexibility improve, but the computational resources and processing time increase
Solution Approach 1:
The system applies partial action by using machine learning models selectively for detection tasks rather than applying full computational resources to all operations. The machine learning model processes only the necessary features and data points required for accurate detection, optimizing the balance between detection accuracy and computational resource consumption by avoiding unnecessary processing.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that detect synthetic user accounts of a digital system via machine learning. For instance, the disclosed systems can utilize a machine learning model to analyze account features that are related to a user account and generate an indication that the user account is synthetic based on the analysis. The disclosed systems can further disable (e.g., suspend or close) the user account based on determining that the user account is synthetic. In some cases, the machine learning model provides a precision score that indicates a likelihood that the user account is synthetic, and the disclosed systems disable the user account if the precision score satisfies a threshold. In some implementations, the disclosed systems generate the machine learning model using synthetic user accounts detected via one or more rules and other user accounts that are associated with those synthetic user accounts.