ML-Based API Security Assessment System
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Solution Overview
Problem
There is a need for an efficient end-to-end security solution to discover, analyze, and remediate APIs in computing environments using machine learning techniques, as APIs are vulnerable to unauthorized access due to their widespread use and access to sensitive data.
Innovation Solution
A machine learning-based system that captures network traffic, retrieves source code from repositories, determines API traffic, and uses a machine learning subsystem to assess whether APIs meet supervisory requirements, invoking remediation protocols when they do not, and addressing exposure vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual API security analysis is performed, then security assessment can be conducted, but it requires significant manual effort and time
Solution Approach 1:
The system enables APIs to be automatically analyzed and assessed through machine learning models without requiring manual security expert intervention. The ML subsystem autonomously processes API traffic, evaluates compliance with supervisory requirements, and generates security assessments, allowing the system to serve itself in the security analysis process.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated machine learning-based analysis. Instead of security professionals manually reviewing API traffic and code, the system uses ML models trained on API behavior patterns to automatically detect security issues and assess compliance, substituting human mechanical analysis with automated intelligent systems.
2Reliability
If comprehensive API security monitoring is implemented, then security coverage is improved, but system complexity increases
Solution Approach 1:
The machine learning subsystem serves multiple security functions simultaneously: it discovers APIs, analyzes traffic patterns, assesses compliance with supervisory requirements, and monitors for security threats. This multi-functional approach allows comprehensive security coverage without proportionally increasing system complexity, as a single ML-based platform performs what would otherwise require multiple separate security tools.
Solution Approach 2:
The system changes the parameter of analysis from static rule-based security checks to dynamic machine learning-based behavioral analysis. By transitioning from fixed security rules to adaptive ML models that learn from API traffic patterns, the system achieves comprehensive security monitoring while managing complexity through intelligent automation rather than numerous complex rules.
3Measurement precision
If machine learning models are trained on extensive API data, then detection accuracy improves, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary data collection and preprocessing during normal API operation, preparing training datasets in advance. By continuously gathering API traffic data, extracting features, and preparing training samples during operational phases, the system reduces the time required for actual model training when security assessments need to be performed, as the groundwork is already completed.
Solution Approach 2:
The machine learning models are trained continuously or incrementally using streaming API data rather than requiring batch training on complete datasets. This continuous learning approach allows the system to maintain high detection accuracy by constantly updating models with new API patterns while avoiding the need for lengthy periodic retraining, thus maintaining both accuracy and operational efficiency.
Data Source
AI summary
Systems, computer program products, and methods are described herein for network traffic discovery and analysis. The present invention is configured to capture data traffic across network ports in a computing environment; retrieve source code from code repositories; determine that the data traffic and the source code are associated with application programming interface (API) traffic; determine a first API associated with the API traffic; determine, using a machine learning (ML) subsystem, whether the first API meets supervisory requirements; and invoke a remediation protocol in an instance when the first API does not meet supervisory requirements.