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Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection.
- Source :
- Vietnam Journal of Computer Science (World Scientific); May2020, Vol. 7 Issue 2, p145-159, 15p
- Publication Year :
- 2020
-
Abstract
- Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection. [ABSTRACT FROM AUTHOR]
- Subjects :
- MALWARE
CYBERCRIMINALS
COMPUTER programmers
FEATURE selection
Subjects
Details
- Language :
- English
- ISSN :
- 21968888
- Volume :
- 7
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Vietnam Journal of Computer Science (World Scientific)
- Publication Type :
- Academic Journal
- Accession number :
- 149042612
- Full Text :
- https://doi.org/10.1142/S2196888820500086