API Account Creation Detection Using AI Risk Scoring
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Solution Overview
Problem
Conventional systems for identifying automated account generation requests are becoming increasingly unsatisfactory due to the use of fixed criteria and manual intervention, leading to inefficient resource consumption and potential system overload by malicious third parties.
Innovation Solution
Implementing a machine learning model trained on a dataset of user accounts generated via programming protocols to analyze API requests, predicting the likelihood of automated account generation and executing additional security protocols based on a sensitivity threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fixed criteria and manual intervention are used to identify automated account generation requests, then system simplicity is maintained, but detection accuracy and reliability deteriorate
Solution Approach 1:
The patent replaces manual intervention and fixed criteria (mechanical systems) with a machine learning model that automatically analyzes API requests. The model uses trained datasets to predict whether requests are generated automatically, substituting human-based detection with an intelligent automated system that improves accuracy without proportionally increasing complexity.
Solution Approach 2:
The system dynamically adjusts detection parameters by using a machine learning model that can adapt to different patterns of automated account generation. The model takes multiple input parameters from API requests and transforms them into a prediction output, allowing the system to respond to evolving malicious techniques without requiring manual reconfiguration.
2Reliability
If additional security protocols are executed for all new user accounts, then system reliability improves, but resource consumption and network activity increase
Solution Approach 1:
The patent applies partial action by executing additional security protocols only for accounts flagged by the machine learning model as potentially automated. Instead of applying security protocols universally, the system selectively applies them based on the model's prediction, reducing unnecessary resource consumption while maintaining reliability for high-risk cases.
Solution Approach 2:
The machine learning model enables the system to self-service by automatically identifying suspicious accounts and triggering appropriate security measures without requiring manual review. This automation reduces the burden on human operators and optimizes resource allocation by focusing security efforts where they are most needed.
3Productivity
If automated account generation requests are blocked using fixed criteria, then productivity is improved, but false positives increase and human user requests may be denied
Solution Approach 1:
The patent replaces fixed criteria and manual review processes with a machine learning model that can distinguish between automated and human-generated requests. The model analyzes patterns in API requests and provides predictions that guide automated decision-making, improving both efficiency and accuracy compared to rigid fixed criteria.
Solution Approach 2:
The system transitions from static fixed criteria to dynamic, adaptive detection using a machine learning model. The model can learn from new data and adapt to evolving patterns of automated account generation, allowing the system to maintain high productivity while reducing false positives through continuous improvement of its detection capabilities.
4Measurement precision
If manual intervention is used to review account generation requests, then detection accuracy improves, but processing time and operational complexity increase
Solution Approach 1:
The patent substitutes manual review processes with an automated machine learning model that can analyze requests in real-time. The model processes multiple parameters from API requests and generates predictions automatically, eliminating the time loss associated with manual intervention while maintaining or improving detection accuracy through consistent, data-driven analysis.
Solution Approach 2:
The machine learning model serves as an intermediary between automated account generation requests and final approval decisions. It processes requests automatically and provides predictions that can be used to guide further action, reducing the need for direct manual intervention while maintaining detection accuracy through the model's analytical capabilities.
Data Source
AI summary
Disclosed herein are systems and methods for detecting automated account generation requests. An example method includes receiving an application programming interface (API) request to generate a new user account. The method then includes executing a machine learning model to predict a likelihood of the API request having been generated automatically using one or more programming protocols. The machine learning model may be trained using historic requests known to have been generated using a machine or a programming/algorithm. When the machine learning model determines that the API request is likely to have been machine-made, the method includes executing an additional security protocol associated with the new user account.