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Malware Classification With Machine Learning Enhanced by Windows Kernel Emulation
Enhance your malware detection with machine learning and Windows kernel emulation. Our research explores the effectiveness of this approach, achieving 77% malware detection with minimal false positives.
- Malware classification can be enhanced by Windows kernel emulation for improved detection rates.
- The research used a combination of arms, including static features, file path, and API calls, to build a meta model.
- The use of one-dimensional convolutions and embeddings allowed for the extraction of valuable information from PE files.
- The model was able to detect 77% of malware samples, with a false positive rate of 14%.
- The research demonstrates the potential for AI-based malware detection and classification.
- The use of emulation reports and JSON files can provide valuable information for analysis and classification.
- Additional sources of contextual awareness, such as network connections and registry access, can be used to improve detection rates.
- Advantages of the research include improved detection rates and the ability to analyze malware in a controlled environment.
- Limitations include the need for large amounts of data and the potential for emulation errors.
- Future work includes improving the model through additional research and exploring the use of these techniques in production environments.
- The researchers demonstrated the potential of AI-based malware classification and detection for improved security and AI collaboration.