Query Management in Streaming Systems for Malware Detection
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Solution Overview
Problem
The increasing complexity of malware and its obfuscation techniques make it challenging for security software to detect malicious behavior in a timely and resource-efficient manner.
Innovation Solution
A system for managing queries that monitor data streams from computing devices, allowing for real-time detection of potential security threats by executing queries in a test environment before deployment to a production environment, and using unique query identifiers to control and track queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If security software performs detailed analysis to detect obfuscated malware, then detection accuracy is improved, but time consumption and resource usage increase
Solution Approach 1:
The system performs preliminary actions by executing queries in a test environment before deploying them to production. This allows the system to pre-validate query effectiveness and resource requirements, enabling faster real-time detection without sacrificing accuracy. The test environment execution provides advance information about query performance characteristics.
Solution Approach 2:
The system segments the malware detection process into distinct phases: query development in test environment, query validation, and production deployment. This segmentation allows detailed analysis to be performed separately during testing, while the production system benefits from optimized, pre-validated queries that require less computational overhead during actual detection operations.
2Measurement precision
If security software performs detailed analysis to detect obfuscated malware, then detection accuracy is improved, but resource consumption increases
Solution Approach 1:
The system performs preliminary resource consumption analysis in the test environment before production deployment. By evaluating query resource requirements in advance, the system can optimize query formulations to achieve necessary detection accuracy with reduced resource consumption during production operations.
Solution Approach 2:
The system changes operational parameters by executing queries in a controlled test environment first, allowing adjustment of query parameters, filters, and execution strategies to minimize resource consumption while maintaining detection accuracy. These optimized parameters are then applied in the production environment.
3Productivity
If the system executes multiple queries simultaneously to monitor data streams, then detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments query management by introducing unique query identifiers that distinguish individual queries and their execution states. This segmentation allows the system to track and manage multiple simultaneous queries without increasing overall system complexity, as each query can be independently identified and controlled through its unique identifier.
Solution Approach 2:
The system creates a copy of the production environment as a test environment where queries can be developed and validated before deployment. This copying approach allows complex multi-query operations to be tested and managed in isolation, reducing the complexity burden on the production system while maintaining enhanced detection capability.
Data Source
AI summary
Techniques to manage queries for managing queries that detect activity associated with a data stream of a computing device. A system can receive a request to implement a query from a device, test the query in a test environment, and determine whether or not to deploy the query to a production environment that includes one or more host devices. The system can generate an instruction and/or a query identifier to control a start time and/or an end time for a query.