malware: Malware System Calls
In sboost: Machine Learning with AdaBoost on Decision Stumps
malware R Documentation
Malware System Calls
Description
System call data for apps identified as malware and not malware.
Usage
malware
Format
A data frame with 7597 rows and 361 variables: outcomes 1 if malware, 0 if not.
X1... X360 system calls.
Details
Experimental data generated in this research paper:
M. Dimjašević, S. Atzeni, I. Ugrina, and Z. Rakamarić,
"Evaluation of Android Malware Detection Based on System Calls,"
in Proceedings of the International Workshop on Security and Privacy Analytics (IWSPA), 2016.
Data used for kaggle competition: https://www.kaggle.com/c/ml-fall2016-android-malware
Source
sboost documentation built on May 28, 2022, 1:12 a.m.
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| malware | R Documentation |
Malware System Calls
Description
System call data for apps identified as malware and not malware.
Usage
malware
Format
A data frame with 7597 rows and 361 variables: outcomes 1 if malware, 0 if not. X1... X360 system calls.
Details
Experimental data generated in this research paper:
M. Dimjašević, S. Atzeni, I. Ugrina, and Z. Rakamarić, "Evaluation of Android Malware Detection Based on System Calls," in Proceedings of the International Workshop on Security and Privacy Analytics (IWSPA), 2016.
Data used for kaggle competition: https://www.kaggle.com/c/ml-fall2016-android-malware
Source
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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