malware

System call data for apps identified as malware and not malware.

datasets

Creates classifier for binary outcomes using Adaptive Boosting
(AdaBoost) algorithm on decision stumps with a fast C++ implementation.
For a description of AdaBoost, see Freund and Schapire (1997)
<doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but
easy to interpret and visualize. Feature vectors may be a combination of
continuous (numeric) and categorical (string, factor) elements. Methods
for classifier assessment, predictions, and cross-validation also included.

Jadon Wagstaff

sboost

Machine Learning with AdaBoost on Decision Stumps

malware function

A data frame with 7597 rows and 361 variables: outcomes 1 if malware, 0 if not.
X1... X360 system calls.

malware: Malware System Calls

Description

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Details