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Data mining methods for detection of new malicious executables
Open accessAbstract
A serious security threat today is malicious executables, especially new, unseen malicious executables often arriving as email attachments. These new malicious executables are created at the rate of thousands every year and pose a serious security threat. Current anti-virus systems attempt to detect these new malicious programs with heuristics generated by hand. This approach is costly and oftentimes ineffective. We present a data mining framework that detects new, previously unseen malicious executables accurately and automatically. The data mining framework automatically found patterns in our data set and used these patterns to detect a set of new malicious binaries. Comparing our detection methods with a traditional signature-based method, our method more than doubles the current detection rates for new malicious executables.
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A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
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Automatic analysis of malware behavior using machine learning
Rieck, K., Trinius, P., Willems, C., & Holz, T. (2011) · Journal of Computer Security · doi:10.3233/jcs-2010-0410 · Cited by 729
A Comprehensive Review on Malware Detection Approaches
Aslan, O., & Samet, R. (2020) · IEEE Access · doi:10.1109/access.2019.2963724 · Cited by 649
A Survey on Malware Detection Using Data Mining Techniques
Ye, Y., Li, T., Adjeroh, D., & Iyengar, S. S. (2018) · ACM Computing Surveys · doi:10.1145/3073559 · Cited by 623
Image-Based malware classification using ensemble of CNN architectures (IMCEC)
Vasan, D., Alazab, M., Wassan, S., Safaei, B., & Zheng, Q. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101748 · Cited by 401
Metadata
- Authors
- Schultz, M. G., Eskin, E., Zadok, F., & Stolfo, S. J.
- Venue
- Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-13
- Pages
- 38-49
- OpenAlex
- W2144112223
- Cited by
- 1,067
- References
- 1
- FWCI
- 9.12
- Type
- conference paper
- Language
- en
- Open access
- green
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- Schultz, M. G., Eskin, E., Zadok, F., & Stolfo, S. J. (2002). Data mining methods for detection of new malicious executables. Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001. https://doi.org/10.1109/secpri.2001.924286
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