Android Malware Detection Using Long Short Term Memory Recurrent Neural Networks

Lilia Georgieva*, Basile Lamarque

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we study the security attacks on Android using Long Short Term Memory (LSTM) Recurrent Neural Networks. As one of the most popular operating systems, Android is a prime target for security attacks. Only in 2019, 10.5 million malware was detected. Recursive neural networks are in essence machine models made up of a list of cells. Their particularity is that part of the output of the previous cell is used as input for the next one. LSTM have shown good results in several areas, for example, text generation, translation, trajectory prediction. Among the recursive neural network models, LSTM is one of the most efficient approaches to sequence classification as it is able to make relations between very distant elements in a sequence. This research explored the application of LSTM for Android malware detection using source code decompiled from the Android Application Package (APK). The approach we have tried is to first extract the instructions from the source code while respecting their execution order as much as possible. We then explored several ways to filter and encode these instructions. For all feature sets we created, we obtained an accuracy greater than 70 % of accuracy and for some feature sets the accuracy reached 83 % showing that it is possible to successfully detect malware using source code and LSTM.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Applied CyberSecurity (ACS) 2021
EditorsHani Ragab Hassen, Hadj Batatia
Number of pages11
ISBN (Electronic)9783030959180
ISBN (Print)9783030959173
Publication statusPublished - 2 Feb 2022
EventInternational Conference on Applied CyberSecurity 2021 - Dubai, United Arab Emirates
Duration: 13 Nov 202114 Nov 2021

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceInternational Conference on Applied CyberSecurity 2021
Abbreviated titleACS 2021
Country/TerritoryUnited Arab Emirates

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


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