Microservices Security Using Generative Anomaly Detection
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Solution Overview
Problem
Web-based environments with microservices architectures face issues such as network congestion, latency, version incompatibility, data integrity, and security threats due to decentralized components, impacting operations and user data security.
Innovation Solution
An intelligent microservices architecture utilizing generative machine-learning models to identify anomalous interservice interaction patterns and dynamically update operations processes, including security, version control, network routing, and self-healing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If microservices architectures with decentralized software components are used, then system reliability and maintainability are improved, but network congestion and latency increase
Solution Approach 1:
The patent introduces an API gateway as an intermediary component that mediates all communication between external clients and internal microservices. The gateway consolidates network traffic handling, implements caching mechanisms, and manages request routing efficiently, thereby reducing network latency while preserving the decentralized architecture's reliability benefits.
Solution Approach 2:
The patent merges multiple microservice functions into service meshes and aggregated service endpoints that handle common operations collectively. By combining authentication, authorization, and routing functions into shared infrastructure components, the system reduces redundant network communications and lowers overall latency while maintaining individual service independence.
2Adaptability or versatility
If multiple independent microservices are deployed, then system adaptability and ease of maintenance are improved, but version incompatibility and data integrity issues arise
Solution Approach 1:
The patent implements dynamic version routing that automatically directs requests to appropriate service versions based on client requirements and service availability. The API gateway maintains multiple version endpoints and intelligently routes traffic, allowing the system to adapt to different version requirements while ensuring data integrity through consistent routing policies.
Solution Approach 2:
The system incorporates feedback mechanisms where the API gateway monitors service health, version compatibility, and data consistency across microservices. When version incompatibilities or data integrity issues are detected, the gateway dynamically adjusts routing decisions and can trigger automated reconciliation processes to maintain system stability.
3Ease of operation
If decentralized microservices components are implemented, then system responsiveness and ease of operation are improved, but security threats and system faults increase
Solution Approach 1:
The patent segments security functions into dedicated microservices including authentication services, authorization services, and encryption services. Each security function operates as an independent, securely isolated microservice that can be updated and patched without affecting other system components, thereby reducing security threats while maintaining system responsiveness.
Solution Approach 2:
The API gateway serves as a security intermediary that implements centralized authentication, authorization, and input validation before requests reach internal microservices. This intermediary layer filters out malicious requests, manages security credentials, and enforces access policies, reducing security threats while preserving the responsive nature of the decentralized architecture.
4Reliability
If generative machine-learning models are executed to identify anomalous patterns, then system security is improved, but processing workload and execution time increase
Solution Approach 1:
The system implements partial execution of generative machine-learning models by applying anomaly detection only to high-risk transactions and patterns identified through preliminary rule-based filtering. The API gateway uses lightweight heuristics to pre-screen requests and directs only suspicious patterns to full machine-learning analysis, thereby improving security while minimizing the processing workload and maintaining system throughput.
Data Source
AI summary
A system includes a memory configured to store a software application and a plurality of microservices including a set of independent software service components configured to be independently executed. The system includes processors operably coupled to the memory and configured to receive a request to initiate an execution of interactions with the software application for satisfying the request, and to identify, based on the request, one or more microservices to be executed to initiate the execution of the interactions with the software application. The processors further execute one or more generative machine-learning models trained to identify anomalous interservice interaction patterns between the one or more microservices. In response to identifying the anomalous interservice interaction patterns, the processors dynamically update operations processes associated with the one or more microservices, and further execute the one or more microservices to initiate the execution of the one or more interactions with the software application.