Detection and Classification of Malwares in Mobile Applications

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)

Abstract

Android is the most widely used as mobile operation system, the thing that make it the target of many attackers, day by day the market of malicious applications develop and grow to reach every applications store. In this purpose many researches have been made to deal with threats through proposing different malware detection techniques. The main objective of this thesis is to detect malwares in android mobiles by applying reverse engineering techniques to unpack malicious apps code and classify them in order to detect whether the app is malicious or not. This paper aims to give a comprehensive account of proposed techniques to detect and classify malicious applications in android mobiles based on machine learning algorithms and a comparison between those techniques in order to conclude the most efficient one and their limitations with a view to ameliorate them.

Keywords

Machine learning Android malicious Detection Classification Reverse engineering 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.IT Department, Faculty of Science and TechnologyUniversity of Abdelmalek Essaadi (UAE)TangierMorocco

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