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Up-High to Down-Low: Applying Machine Learning to an Exploit Database

  • Yisroel Mirsky
  • Noam Gross
  • Asaf Shabtai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9522)

Abstract

Today machine learning is primarily applied to low level features such as machine code and measurable behaviors. However, a great asset for exploit type classifications is public exploit databases. Unfortunately, these databases contain only meta-data (high level or abstract data) of these exploits. Considering that classification depends on the raw measurements found in the field, these databases have been overlooked. In this study, we offer two usages for these high level datasets and evaluate their performance. The first usage is classification by using meta-data as a bridge (supervised), and the second usage is the study of exploits’ relations using clustering and Self Organizing Maps (unsupervised). Both offer insights into exploit detection and can be used as a means to better define exploit classes.

Keywords

Exploit database Machine learning Supervised Unsupervised Pattern abstraction Data mining 

Notes

Acknowledgements

This research was supported by the Ministry of Science and Technology, Israel.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Information Systems EngineeringBen-Gurion UniversityBeershebaIsrael

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