Rate-Limiting API Calls for CRM Accounts via Predicted Abuse
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Solution Overview
Problem
Customer-relationship-management systems are vulnerable to abusive behavior, such as fraudulent API calls that generate spam emails, which existing technologies fail to effectively mitigate.
Innovation Solution
A system that detects abusive accounts by analyzing account-related information and operational parameters, using machine-learning models to determine the likelihood of abusive behavior and subsequently rate-limits API calls to prevent malicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system processes all API calls without rate-limiting, then the customer-relationship-management system maintains high productivity and service automation, but the system becomes vulnerable to fraudulent abuse and spam generation
Solution Approach 1:
The system performs preliminary analysis of account-related information and operational parameters before processing API calls. A machine-learning model predicts the likelihood of abusive behavior in advance, allowing the system to proactively rate-limit suspicious accounts before they can execute fraudulent activities, thus preventing harm while maintaining productivity for legitimate accounts
Solution Approach 2:
The rate-limiting mechanism applies different processing rates to different accounts based on their individual risk profiles. Instead of uniformly limiting all API calls, the system dynamically adjusts rate limits for each account based on the predicted likelihood of abusive behavior, allowing high productivity for trustworthy accounts while restricting potentially malicious ones
2Reliability
If the system implements rate-limiting for all accounts, then fraudulent behavior is prevented, but legitimate customer-service interactions are unnecessarily restricted
Solution Approach 1:
The system changes the parameter of API call processing rate dynamically based on account characteristics and behavior patterns. The machine-learning model continuously evaluates account-related information and operational parameters, adjusting the rate-limiting parameter in real-time to reflect the current risk level of each account, ensuring legitimate operations are not unnecessarily restricted
Solution Approach 2:
The system replaces manual or rule-based rate-limiting mechanisms with a machine-learning model that automatically predicts abusive behavior. This substitution enables more nuanced and accurate differentiation between legitimate and fraudulent accounts, improving ease of operation for legitimate users while maintaining protection against fraud
3Measurement precision
If the system analyzes detailed account-related information to detect abuse, then detection precision improves, but the complexity of the detection system increases
Solution Approach 1:
The machine-learning model serves multiple functions: it analyzes diverse account-related information, predicts abusive behavior, and outputs risk scores that drive rate-limiting decisions. This single multi-functional component handles various detection tasks uniformly, improving measurement precision without proportionally increasing system complexity
Data Source
AI summary
The disclosed embodiments relate to a system that detects and controls operations performed by abusive accounts in a customer-relationship-management system. During operation, the system obtains account-related information for an account in the customer-relationship-management system. Next, the system determines a likelihood that the account is being used for abusive purposes based on the account-related information. Finally, the system limits a rate of processing for application-programming interface (API) calls made from the account to the customer-relationship-management system based on the determined likelihood that the account is being used for abusive purposes.