Modeling and Selecting Optimal Features for Machine Learning Based Detections of Android Malwares


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 11, pp. 427-432, Nov. 2019
https://doi.org/10.3745/KTSDE.2019.8.11.427, Full Text:
Keywords: Android, Malware, APK, AI, Ensemble Algorithm
Abstract

In this paper, we propose three approaches to modeling Android malware. The first method involves human security experts for meticulously selecting feature sets. With the second approach, we choose 300 features with the highest importance among the top 99% features in terms of occurrence rate. The third approach is to combine multiple models and identify malware through weighted voting. In addition, we applied a novel method of eliminating permission information which used to be regarded as a critical factor for distinguishing malware. With our carefully generated feature sets and the weighted voting by the ensemble algorithm, we were able to reach the highest malware detection accuracy of 97.8%. We also verified that discarding the permission information lead to the improvement in terms of false positive and false negative rates.


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Cite this article
[IEEE Style]
K. W. Lee, S. T. Oh and Y. Yoon, "Modeling and Selecting Optimal Features for Machine Learning Based Detections of Android Malwares," KIPS Transactions on Software and Data Engineering, vol. 8, no. 11, pp. 427-432, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.11.427.

[ACM Style]
Kye Woong Lee, Seung Taek Oh, and Young Yoon. 2019. Modeling and Selecting Optimal Features for Machine Learning Based Detections of Android Malwares. KIPS Transactions on Software and Data Engineering, 8, 11, (2019), 427-432. DOI: https://doi.org/10.3745/KTSDE.2019.8.11.427.