Malware Detection via Trigger Scenario Simulation
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Solution Overview
Problem
Current malware detection methods, such as signature and hash sum analysis, fail to detect modified or obfuscated malware, and behavioral analysis can be ineffective and resource-intensive, as they require triggering malicious software, potentially allowing harm to occur before detection.
Innovation Solution
The system performs behavioral malware analysis using malware trigger scenarios, which involve creating specific events to activate malicious behavior in a controlled environment, monitoring execution across multiple system levels, and selecting optimized scenarios for detection, including remedial actions like blocking API calls and quarantining malicious programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If behavioral analysis is used to detect malware, then detection effectiveness against modified and obfuscated malware is improved, but system productivity deteriorates due to resource-intensive execution monitoring
Solution Approach 1:
The system performs preliminary static analysis of the software program to identify suspicious code patterns, obfuscation techniques, and potential malicious behaviors before execution. This preliminary assessment allows the system to prioritize which programs require full behavioral analysis, reducing the overall monitoring burden on system productivity while maintaining high detection effectiveness for truly malicious software.
Solution Approach 2:
The system introduces an intermediary layer that combines static analysis results with behavioral analysis priorities. This intermediary assessment mechanism filters and prioritizes programs for monitoring, allowing the system to focus behavioral analysis resources on high-risk programs while maintaining detection effectiveness through the combined approach.
2Measurement precision
If behavioral analysis monitors all program execution, then detection precision is improved, but time consumption increases due to continuous monitoring overhead
Solution Approach 1:
The system performs preliminary static analysis to identify programs with high malware probability based on code patterns, headers, and structural characteristics. This preliminary sorting allows the system to apply intensive behavioral monitoring only to high-risk programs, reducing overall detection time while maintaining high precision for malicious software identification.
Solution Approach 2:
The system applies behavioral monitoring at different intensities based on risk assessment. For high-risk programs identified through static analysis, the system performs comprehensive behavioral monitoring with detailed event tracking. For low-risk programs, the system applies minimal monitoring or none at all, optimizing the balance between detection precision and time consumption.
3Reliability
If trigger events are created to activate malicious behavior, then detection capability is improved, but system security worsens as malware may inflict harm before detection
Solution Approach 1:
The system performs preliminary static analysis to identify suspicious programs before creating any trigger events. Only programs flagged as high-risk through static analysis undergo behavioral analysis with trigger events. This preliminary filtering ensures that trigger events are created only for programs already identified as potentially malicious, reducing the risk of harm to the system while maintaining detection capability.
Solution Approach 2:
The system introduces an intermediary risk assessment layer that evaluates programs based on static analysis characteristics before allowing trigger event creation. This intermediary mechanism acts as a safety filter, ensuring that behavioral analysis with potentially harmful trigger events is applied only to programs that have already been identified as high-risk through safer static analysis methods.
4Reliability
If comprehensive behavioral analysis is performed on all programs, then detection coverage is improved, but device complexity increases due to multiple monitoring layers
Solution Approach 1:
The system segments the malware detection process into distinct stages: static analysis stage and behavioral analysis stage. Each stage has specific monitoring objectives and methods. This segmentation allows the system to achieve comprehensive detection coverage by combining results from both stages while managing complexity through modular, stage-specific implementation rather than a single monolithic analysis system.
Solution Approach 2:
The system creates a multi-functional analysis framework where the static analysis component serves universal screening purposes for all programs, while the behavioral analysis component provides deep inspection for high-risk programs. This multi-functional approach achieves comprehensive detection coverage through different analysis methods working together, while managing complexity by assigning specific functions to each analysis layer.
Data Source
AI summary
Disclosed system and methods for malware testing of software programs. An example method includes storing a plurality of malware trigger scenarios specifying different sets of malware trigger events known to trigger malicious behavior in software programs; in response to obtaining a software program, modifying a computer environment for operating the software program by creating malware trigger events associated with a selected one of the plurality of malware trigger scenarios; analyzing an execution of the software program in the modified computer environment in response to the malware trigger events; upon detecting that the software program exhibits malicious behavior, performing remedial actions on the software program; and upon detecting that the software program exhibits no malicious behavior, selecting a different malware trigger scenario from the plurality of malware trigger scenarios for malware testing of the software program.