MIGO Event Pipeline for Dynamic Multi-Instance Behavior Detection
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Solution Overview
Problem
Conventional behavioral detection schemes are limited in their ability to detect and respond to complex computer behaviors beyond mere textual matches, requiring extensive manual effort and lacking flexibility in handling diverse use cases.
Innovation Solution
The Multi-Instance Generic Operation (MIGO) pipeline dynamically configures and deploys templates that can process non-textual data, allowing for sophisticated and concise event detection and response, with support for multiple dispositions and extensible interfaces to handle various scenarios efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional behavioral detection schemes are used, then simple textual matches can be detected, but complex computer behaviors cannot be effectively detected and responded to
Solution Approach 1:
The detection system is segmented into separate components: templates for defining behaviors, an agent for monitoring events, and a pipeline for processing matches. This allows complex detection capabilities to be achieved through composition of simpler elements, resolving the contradiction between detection versatility and system complexity.
Solution Approach 2:
The template pipeline serves multiple functions: it processes matched events, implements template dispositions, and handles various response actions. This multi-functionality allows a single system to address complex detection needs without proportionally increasing complexity, as one component handles multiple detection and response tasks.
2Measurement precision
If manual configuration of detection templates is performed, then detection accuracy can be improved, but implementation time and manual effort increase significantly
Solution Approach 1:
Templates are pre-defined and stored in a repository before actual detection needs arise. When detection is required, the system retrieves and executes pre-configured templates through the pipeline, eliminating the need for manual configuration at runtime and reducing implementation time while maintaining accuracy.
Solution Approach 2:
Instead of manually creating detection configurations for each scenario, the system uses template copying and reuse. Templates can be replicated across multiple instances and devices, allowing rapid deployment of detection capabilities without manual reconfiguration for each use case.
3Adaptability or versatility
If flexible template processing is implemented, then adaptability to diverse use cases improves, but system complexity and difficulty of operation increase
Solution Approach 1:
The template pipeline automatically processes matched events and executes template dispositions without requiring manual intervention. The system self-manages the complexity of template processing, event matching, and response execution, presenting a simplified interface while maintaining high flexibility for diverse use cases.
Data Source
AI summary
An event detection service detects hardware and software events at endpoint devices. The event detection service deploys templates to agents in the field. Each template is created in the cloud to describe kernel-mode and user-mode events of interest. Each agent installs the templates without rebooting. Each agent monitors its host's event behaviors according to the templates. If the host's event behaviors satisfy the template, then the agent has a Multi-Instance Generic Operation pipeline that determines a template disposition specified by the template. The agent may thus dynamically detect event behaviors for a purpose, as specified by the template.