Automated Generation and Selection of Interpretable Features for Enterprise Security

BigData, pp. 1258-1265, 2018.

被引用2|引用|浏览45|DOI:https://doi.org/10.1109/BigData.2018.8621986
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其它链接dblp.uni-trier.de|academic.microsoft.com

摘要

We present an effective machine learning method for malicious activity detection in enterprise security logs. Our method involves feature engineering, or generating new features by applying operators on features of the raw data. We generate DNF formulas from raw features, extract Boolean functions from them, and leverage Fourier analysis ...更多

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