Deep learning for detecting Android malwares
Soussi Ilham, Abderrahim Ghadi, Boudhir Anouar Abdelhakim · Proceedings of the 4th International Conference on Smart City Applications · 2019
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.