Deep learning for detecting Android malwares
Article No.: 104, Pages 1 - 4
Abstract
The revolution and development of malwares over time necessitate an intensive researches on advanced techniques to secure user's personal and critical information, the most challenging task is to build a strong and robust classifier allows to detect different types of malwares and being able to defeat zero-day malware attacks. Machine learning algorithms as SVM (support vector machine), Random Forest and Naïve Bayes are well-known choices for building the malware classifier, even though the deep learning which is a subfield of machine learning, has a portion in classifying android malwares with high precision. In this paper we present a modest study on difference between using both techniques and proposition of an approach based on deep learning technique applied on Apk of android applications belong to a heterogeneous data combined of benign and malware applications of different types.
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October 2019
788 pages
ISBN:9781450362894
DOI:10.1145/3368756
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Association for Computing Machinery
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Published: 02 October 2019
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SCA2019
SCA2019: The Fourth International Conference on Smart City Applications
October 2 - 4, 2019
Casablanca, Morocco
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