API Gateway Risk Scoring for Unauthorized Access Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current authentication and authorization systems face challenges in effectively detecting and preventing unauthorized access to APIs, particularly due to vulnerabilities in password-based systems and the impact of multi-factor authentication on user interaction, which can lead to false positives and negatives.
Innovation Solution
A multilayered authorization access system that assigns risk scores to API traffic based on user device information, behavior patterns, and back-end service responses, using supervised machine learning to generate confidence scores and perform incremental authentication, thereby providing early warning and continuous protection before, during, and after the API gateway.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If password-based authentication and multi-factor authentication are used, then security level is improved, but user interaction complexity and false positives increase
Solution Approach 1:
The system performs automatic risk assessment and authentication decisions without requiring user interaction. The machine learning model autonomously evaluates API traffic, assigns risk scores, and determines authentication requirements based on observed patterns, eliminating the need for users to manually complete multi-factor authentication steps while maintaining security.
Solution Approach 2:
The patent replaces traditional mechanical authentication mechanisms (password verification, multi-factor authentication challenges) with an intelligent system based on machine learning and behavioral analysis. The system substitutes direct user interaction with automated risk assessment algorithms that analyze traffic patterns, device information, and behavior sequences to make authentication decisions.
2Reliability
If traditional authentication systems are used, then security is provided, but false positives and negatives increase
Solution Approach 1:
The system performs preliminary risk assessment by observing API traffic before it reaches the gateway. By analyzing requests in advance, assigning initial risk scores based on device and behavior patterns, and preparing authentication challenges proactively, the system reduces false positives by pre-identifying suspicious activity rather than reacting to it after authentication attempts fail.
Solution Approach 2:
The system continuously monitors API traffic, compares observed behavior against learned patterns, and adjusts risk scores dynamically based on feedback from the environment. The machine learning model learns from authentication outcomes and traffic patterns, refining its predictions to reduce false positives and negatives over time through continuous adaptation.
3Adaptability or versatility
If risk-based authentication with IP address is used, then authorization is improved, but detection capability against sophisticated attacks deteriorates
Solution Approach 1:
The system moves beyond single-dimensional IP address-based risk assessment by incorporating multiple dimensions of analysis including device information, behavior patterns, request sequences, and temporal patterns. This multi-dimensional approach enables detection of sophisticated attacks that may originate from legitimate IP addresses by analyzing patterns across multiple variables simultaneously.
Solution Approach 2:
The system dynamically adjusts risk scores and authentication requirements based on real-time observations of traffic patterns and behavior. Rather than using static IP-based rules, the system adapts its detection capabilities by learning from observed behavior, allowing it to detect sophisticated attacks that evolve over time while maintaining flexible authorization decisions.
Data Source
AI summary
An authorization access system and method of minimizing unauthorized access to a resource are provided. The authorization access system comprises at least one processor, and a memory storing instructions which when executed by the at least one processor configure the at least one processor to perform the method. The method comprises assigning a first risk score to application programming interface (API) traffic associated with a user device and/or user behaviour pattern observed prior to an API gateway, assigning a second risk score to the API traffic associated with the user device observed at the API gateway, assigning a third risk score to the API traffic associated with the user device and/or back end service responses observed after the API gateway, and performing an authorization action based on any of the first, second or third risk scores.