Malware Trigger Scenario Detection for Obfuscated Software
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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 employs behavioral malware analysis using malware trigger scenarios, which involve specifying and executing different sets of trigger events to monitor and analyze software behavior, allowing for the identification of malicious activities and remedial actions, while optimizing scenarios for improved detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If behavioral analysis is used to detect modified malware, then detection effectiveness is improved, but system productivity deteriorates due to resource-intensive triggering and monitoring processes
Solution Approach 1:
The system performs preliminary static analysis of malware code structure, APIs, and configurations before behavioral triggering. This preliminary characterization creates a baseline profile that enables faster, more targeted behavioral monitoring, reducing the overall resource consumption while maintaining detection effectiveness
Solution Approach 2:
The analysis process is segmented into distinct phases: static code analysis, API call pattern analysis, configuration file analysis, and targeted behavioral monitoring. Each phase focuses on specific aspects of malware detection, reducing the computational burden of any single phase while improving overall detection precision
2Reliability
If behavioral analysis is used to detect malware, then detection capability is improved, but harmful factors worsen because malware can inflict harm before being detected
Solution Approach 1:
The system performs preliminary static analysis of the malware code, APIs, and configuration files before triggering any behavioral execution. This early analysis identifies suspicious patterns and characteristics that can flag potentially harmful software before it has a chance to execute malicious actions
Solution Approach 2:
The system prepares remedial actions and response protocols in advance based on the static analysis results. If suspicious patterns are detected during preliminary analysis, the system can immediately implement protective measures or isolate the software, preventing harm before behavioral execution occurs
3Measurement precision
If comprehensive behavioral monitoring is implemented, then malware detection accuracy is improved, but device complexity increases due to multiple trigger scenarios and monitoring mechanisms
Solution Approach 1:
The detection system is segmented into modular components: static analysis module, API analysis module, configuration analysis module, and behavioral monitoring module. Each module handles specific aspects of analysis independently, making the complex system more manageable and maintainable while preserving comprehensive detection capability
Solution Approach 2:
The system dynamically adjusts the depth and scope of monitoring based on risk assessment from preliminary analysis. High-risk software receives comprehensive multi-scenario behavioral monitoring, while low-risk software undergoes lighter monitoring, optimizing the balance between detection accuracy and system complexity
Data Source
AI summary
Disclosed system and methods for detecting malware by performing behavioral malware analysis using malware trigger scenarios. In one aspect, a method for malware detection includes providing a plurality of malware trigger scenarios specifying different sets of malware trigger events known to trigger malicious behavior in malicious software. The method further includes executing a software program in a computer environment and creating one more malware trigger events as specified in the malware trigger scenarios. The method further includes monitoring execution events of the software program in the computer environment and determining based on the analysis of the monitored execution events whether the software program exhibits malicious behavior. When the software program exhibits malicious behavior, performing remedial actions on the software program. When the software program does not exhibit malicious behavior, selecting another scenario from the plurality of malware trigger scenarios for malware testing of the software program.