Microservice Traffic Pattern Detection for Unauthorized Data Transfers
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Solution Overview
Problem
In microservices networks, there is a lack of centralized control over data transfers between loosely coupled microservice entities, leading to risks of rogue or unintentional traffic that can result in data breaches and regulatory non-compliance.
Innovation Solution
A transfer manager is implemented to predict and prevent unauthorized data transfers by generating data traffic patterns based on call logs, using machine learning models to identify unauthorized transfers, and raising alerts to reconfigure data exchanges before they occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If microservice entities are allowed to communicate freely with minimal centralized management, then system flexibility and scalability are improved, but unauthorized data transfers and security risks increase
Solution Approach 1:
The patent introduces a transfer manager as an intermediary component that sits between microservice entities and monitors/controls data transfers. This mediator analyzes traffic patterns, generates predictions about unauthorized transfers, and raises alerts without preventing the flexible communication architecture itself, thus resolving the contradiction between flexibility and security
Solution Approach 2:
The system performs preliminary actions by generating data traffic patterns and predictions about potential unauthorized transfers before they occur. By analyzing historical call logs and creating predictive models, the system prepares security measures in advance, allowing the microservices to maintain flexibility while security checks are already in place
2Reliability
If centralized control is implemented to prevent unauthorized data transfers, then data security is improved, but system complexity increases
Solution Approach 1:
The transfer manager operates autonomously by automatically analyzing call logs, generating traffic patterns, making predictions about unauthorized transfers, and raising alerts without requiring constant human intervention. This self-service approach improves security while minimizing the operational complexity that would otherwise be required for centralized control
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring data transfers, comparing actual traffic against predicted patterns, and adjusting its analysis accordingly. This automated feedback loop enables effective centralized control through a relatively simple system that adapts to changing traffic patterns without requiring complex manual management
3Measurement precision
If predictive analysis is performed to identify unauthorized transfers, then security detection capability is improved, but processing resources are consumed
Solution Approach 1:
The transfer manager applies partial analysis by focusing computational resources on generating predictions for specific data transfer patterns rather than analyzing all possible transfers in equal detail. By identifying and prioritizing high-risk patterns based on historical data, the system achieves good detection accuracy while consuming fewer processing resources than a comprehensive analysis would require
Data Source
AI summary
A plurality of data traffic patterns are generated based on call logs associated with previous data exchanges between a first microservice entity and a second microservice entity. It is determined based on a data policy that the second microservice entity is not authorized to consume a portion of the data exposed by the first microservice entity. In response, a first data traffic pattern of the plurality of data traffic patterns is identified that is associated with a first known data transfer or a first potential data transfer in which the second microservice entity consumes the portion of the data exposed by the first microservice entity. An alert associated with the first data traffic pattern is generated.