Run-time Introspection for Distributed Threat Detection
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Solution Overview
Problem
Distributed computing systems face challenges in identifying and mitigating security vulnerabilities due to complex interactions among various resource types, making it difficult to track security issues and automate mitigation, especially as the system evolves and becomes more complex.
Innovation Solution
Implementing introspection points within a distributed computing system to collect and analyze data on network interactions, software libraries, and resource usage, generating a graph to identify potential vulnerabilities and rule violations, and performing security actions such as updating policies or notifying entities to mitigate risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed computing systems are updated and complexity increases, then functionality and resource types are improved, but security vulnerability identification and tracking become more difficult
Solution Approach 1:
The system segments security monitoring into multiple introspection points distributed across different resources (virtual machine instances, data storage resources, network resources). Each introspection point independently collects and analyzes security data from its specific resource, breaking down the complex task of securing the entire distributed system into manageable, localized segments that can be monitored and controlled individually.
2Productivity
If system complexity and usage increase, then resource capabilities are improved, but automated mitigation of vulnerabilities becomes challenging
Solution Approach 1:
The distributed computing system implements self-service security mechanisms where each resource equipped with introspection points autonomously monitors its own security state, identifies vulnerabilities, and executes mitigation actions without requiring external intervention. The system automatically generates security policies, detects rule violations, and performs corrective actions based on predefined security rules, enabling automated mitigation that scales with system complexity.
3Measurement precision
If introspection points collect comprehensive data on network interactions and resource usage, then threat detection accuracy is improved, but system complexity increases
Solution Approach 1:
The introspection points are designed as universal, multi-functional components that can be deployed across diverse resource types (virtual machine instances, data storage resources, network resources) while performing consistent security monitoring functions. Each introspection point universally collects data on network interactions, resource usage, and security events, enabling standardized threat detection across the heterogeneous distributed system without requiring complex, resource-specific monitoring solutions.
Data Source
AI summary
A graph of a plurality of resources in a computing environment is generated, with the graph associating a first resource of the plurality with a second resource of the plurality. Based at least in part on measurements obtained at a point in a test computing environment that corresponds to a point in the computing environment, a graph representing the relationship between the first resource and the second resource is generated. A threat model identifying potential risks to the computing environment is created from the graph.