Metadata-Driven Detection Dictionary for Multi-Environment Security
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Solution Overview
Problem
Protecting computing devices in corporate and enterprise environments from malicious attacks and data breaches remains challenging due to the complexity of managing security across multiple operating environments.
Innovation Solution
A detection dictionary system that supports anomaly detection across multiple operating environments by maintaining a framework of metadata-driven detections, allowing for consistent definition and implementation of security guarantees, detection instances, and anomaly resolution across various platforms, enabling efficient monitoring and alerting of potential threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security detections are implemented across multiple operating environments using separate custom solutions, then each environment can be protected, but the time and cost of developing and maintaining security detections increases significantly
Solution Approach 1:
The patent creates a universal detection framework that can operate across multiple operating environments (Windows, Linux, macOS, etc.) using a common metadata-driven architecture. The system defines detections in environment-agnostic terms with metadata that describes detection logic, data collection requirements, and rendering preferences, allowing the same detection framework to serve multiple platforms without requiring separate custom development for each environment.
Solution Approach 2:
The system enables copying and reusing detection definitions across different operating environments. Once a detection is defined in one environment, its metadata structure can be replicated and adapted to other environments, reducing redundant development effort. The framework maintains detection definitions as reusable templates that can be instantiated across multiple platforms with minimal modification.
2Adaptability or versatility
If separate custom detection solutions are developed for each operating environment, then environment-specific requirements can be met, but consistency in anomaly detection across environments deteriorates
Solution Approach 1:
The patent enforces homogeneity in detection definitions by requiring all detections to conform to a standardized metadata schema regardless of the target operating environment. The metadata structure includes consistent fields for detection logic, data collection specifications, anomaly criteria, and rendering preferences. This homogeneous framework ensures that detections are defined and evaluated consistently across Windows, Linux, macOS, and other environments, eliminating variability introduced by custom implementations.
3Measurement precision
If comprehensive security monitoring is implemented across all operating environments, then anomaly detection capability improves, but the complexity of managing and coordinating detections across environments increases
Solution Approach 1:
The patent introduces an intermediary metadata layer that sits between the detection logic and the various operating environments. This metadata framework acts as a mediator that translates high-level detection requirements into environment-specific implementation details. The metadata includes structured definitions of data collection requirements, anomaly detection logic, and rendering preferences, which the system automatically processes to generate environment-specific detection configurations, thereby reducing the complexity of managing multi-environment detections.
4Reliability
If multiple custom detection frameworks are maintained for different operating environments, then each environment can be optimized, but the cost of development and maintenance increases
Solution Approach 1:
The patent merges the functionality of multiple environment-specific detection frameworks into a single unified framework. By combining the detection logic, metadata management, and rendering capabilities into one system that supports multiple operating environments, the patent eliminates the need to maintain separate custom frameworks for each platform. This consolidation reduces development and maintenance costs while preserving detection effectiveness across all supported environments through the standardized metadata-driven approach.
Data Source
AI summary
A detection dictionary system provides a framework for describing, detecting, and reporting anomalies across multiple operating environments each including multiple computing devices. An anomaly in an operating environment refers to one or more operations or activities in the operating environment that may be indicative of an attack on the operating environment by a malicious user or program. The framework includes guarantees, detections, properties, and detection instances. Guarantees are promises or assertions made to an entity (e.g., a business or other organization) that describes what the detection dictionary system will detect and alert on when a particular trend or anomaly is identified. A detection is a set of metadata describing how to fulfill a given guarantee. A property describes how to map the detection to a particular detection instance. A detection instance is a specific implementation of a detection as applied to a property.