API Gateway Security Using ML Pattern Detection
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Solution Overview
Problem
Existing computer network security systems are inefficient in blocking advanced or new catastrophic attacks, often requiring complex configurations and failing to identify unacceptable data effectively, especially when faced with unfamiliar attack signatures.
Innovation Solution
An intelligent API gateway system that uses a machine learning model to determine URL patterns and data signatures for requests and responses, learning over time to achieve high accuracy in identifying potential security vulnerabilities and automatically blocking suspicious data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing security systems use traditional signature-based methods, then they can block known attacks, but they fail to block advanced or new catastrophic attacks
Solution Approach 1:
The system performs self-learning by automatically analyzing request patterns and data signatures without requiring manual configuration updates. The machine learning model continuously adapts to new attack patterns by learning from incoming traffic, enabling the system to detect and block advanced or novel attacks autonomously
Solution Approach 2:
The patent replaces traditional mechanical signature-matching security systems with an intelligent machine learning-based system. Instead of relying on pre-defined attack signatures, the system uses automated pattern recognition and anomaly detection to identify threats, substituting manual rule-based approaches with adaptive computational intelligence
2Reliability
If existing security systems implement comprehensive protection, then security coverage improves, but configuration complexity increases
Solution Approach 1:
The system automatically learns and adapts to legitimate traffic patterns and service behaviors without requiring manual configuration. By performing self-service learning during normal operation, the system achieves comprehensive security coverage while eliminating the need for complex setup and maintenance configurations
Solution Approach 2:
The system dynamically adjusts its detection parameters and thresholds based on learned patterns rather than using fixed, pre-configured values. This allows the security system to adapt to changing traffic conditions and attack patterns without requiring manual parameter tuning or complex configuration management
3Measurement precision
If machine learning models learn continuously over predefined period, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary learning during off-peak periods or initialization phases to establish baseline patterns before entering production. By preparing the model in advance with preliminary training data, the system achieves high detection accuracy while minimizing the impact of learning operations on real-time request processing
Solution Approach 2:
The machine learning model performs periodic updates and retraining at scheduled intervals rather than continuously during operation. This periodic action allows the system to maintain high detection accuracy through regular model improvements while avoiding continuous processing overhead that would impact real-time security decision-making
Data Source
AI summary
The invention relates to method and system for enhancing computer network security. The method includes receiving a plurality of requests from client devices to avail a plurality of responses from services running on servers; determining a URL pattern for each of the plurality of requests based on URL associated with that request; determining a request data signature for each of the plurality of requests or a response data signature for each of the plurality of responses based on a set of request parameters associated with that request or based on a set of response parameters associated with that response, respectively, using a first machine learning model; and determining an authenticity of each of the plurality of requests based on the URL pattern and the data signature associated with that request, or an authenticity of each of the plurality of responses based on the data signature associated with that response.