Malware Detection via Windows Prefetch File Analysis
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Solution Overview
Problem
Current malware detection methods, particularly static signature-based approaches, are ineffective against evolving malware variants due to obfuscation techniques, and behavioral detection faces challenges in replicating dynamic execution environments and adapting to system changes.
Innovation Solution
A malware detection framework utilizing prefetch files from Windows Prefetch folders, employing machine learning techniques such as Bag of Words, TF-IDF, Singular Value Decomposition, and Logistic Regression for feature extraction and classification, to differentiate between benign and malicious applications, while being resilient to countermeasures like mimicry attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static signature-based malware detection methods are used, then detection simplicity is maintained, but detection accuracy deteriorates against evolving malware variants due to obfuscation techniques
Solution Approach 1:
The patent replaces static signature-based detection (mechanical comparison of known patterns) with behavioral analysis based on prefetch file monitoring. Instead of comparing malware samples against known signatures, the system monitors application launch behaviors and prefetch file modifications to detect malicious activities, thereby improving detection accuracy against obfuscated malware while maintaining operational simplicity.
Solution Approach 2:
The patent introduces prefetch files as an intermediary element for malware detection. Rather than directly analyzing malware code or comparing signatures, the system uses prefetch files as a mediator to indirectly detect malicious behaviors through monitoring application launch patterns and prefetch file modifications, achieving better detection accuracy without increasing system complexity.
2Measurement precision
If behavioral detection methods are used to improve malware detection accuracy, then detection accuracy improves, but system complexity increases due to replicating dynamic execution environments
Solution Approach 1:
The patent extracts the essential behavioral indicators from complex execution environments by focusing specifically on prefetch file modifications and application launch patterns. Instead of replicating entire dynamic execution environments, the system extracts and monitors key behavioral signals in the prefetch folder, thereby maintaining high detection accuracy while significantly reducing system complexity.
Solution Approach 2:
The patent leverages the Windows Prefetch service's existing functionality to achieve behavioral detection. By monitoring the prefetch files that the Windows system already generates and maintains during normal operation, the system obtains behavioral data without needing to replicate execution environments or add complex monitoring infrastructure, thus improving detection accuracy with minimal increase in system complexity.
3Ease of operation
If traditional anti-virus software is used, then ease of operation is maintained, but detection reliability deteriorates against fileless malware and evasion techniques
Solution Approach 1:
The patent uses prefetch files as an intermediary to detect fileless malware that traditional anti-virus software misses. Since fileless malware resides in memory and leaves no traditional file traces, the system monitors prefetch file modifications as an indirect indicator of malicious activity, thereby improving detection reliability while maintaining the simplicity of traditional anti-virus operation.
Solution Approach 2:
The patent replaces traditional file-based scanning mechanisms with behavioral monitoring of prefetch files. Instead of scanning files for known signatures, the system monitors changes in prefetch file timestamps and contents to detect malicious behaviors, improving reliability against fileless malware while keeping the user interface and operation mode similar to traditional anti-virus software.
4Measurement precision
If prefetch file monitoring is implemented for malware detection, then detection accuracy improves, but processing time increases due to analyzing prefetch file features
Solution Approach 1:
The patent extracts only the most discriminative features from prefetch files, such as modification timestamps, file names, and launch patterns, rather than analyzing entire file contents. This selective extraction of key features maintains high detection accuracy while significantly reducing processing time compared to comprehensive prefetch file analysis.
Solution Approach 2:
The patent applies partial monitoring by focusing on specific prefetch file attributes and only analyzing files that show suspicious modification patterns. Instead of continuously analyzing all prefetch files, the system monitors only those with relevant changes, achieving good detection accuracy with reduced processing overhead and time loss.
Data Source
AI summary
A behavioral malware detection involves extracting features from prefetch files, wherein prefetch files; classifying and detecting benign applications from malicious applications using the features of the prefetch files; and quarantining malicious applications based on the detection.