Version 1
: Received: 1 August 2018 / Approved: 2 August 2018 / Online: 2 August 2018 (06:12:48 CEST)
How to cite:
Yen, Y.; Sun, H. An Android Mutation Malware Detection Based on Deep Learning Using Visualization of Importance from Codes. Preprints2018, 2018080034. https://doi.org/10.20944/preprints201808.0034.v1
Yen, Y.; Sun, H. An Android Mutation Malware Detection Based on Deep Learning Using Visualization of Importance from Codes. Preprints 2018, 2018080034. https://doi.org/10.20944/preprints201808.0034.v1
Yen, Y.; Sun, H. An Android Mutation Malware Detection Based on Deep Learning Using Visualization of Importance from Codes. Preprints2018, 2018080034. https://doi.org/10.20944/preprints201808.0034.v1
APA Style
Yen, Y., & Sun, H. (2018). An Android Mutation Malware Detection Based on Deep Learning Using Visualization of Importance from Codes. Preprints. https://doi.org/10.20944/preprints201808.0034.v1
Chicago/Turabian Style
Yen, Y. and Hung-Min Sun. 2018 "An Android Mutation Malware Detection Based on Deep Learning Using Visualization of Importance from Codes" Preprints. https://doi.org/10.20944/preprints201808.0034.v1
Abstract
Using smartphone especially android platform has already got eighty percent market shares, due to aforementioned report, it becomes attacker’s primary goal. There is a growing number of private data onto smart phones and low safety defense measure, attackers can use multiple way to launch and to attack user’s smartphones.(e.g. Using different coding style to confuse the software of detecting malware). Existing android malware detection methods use multiple features, like safety sensor API, system call, control flow structure and data information flow, then using machine learning to check whether its malware or not. These feature provide app’s unique property and limitation, that is to say, from some perspectives it might suit for some specific attack, but wouldn’t suit for others. Nowadays most malware detection methods use only one aforementioned feature, and these methods mostly analysis to detect code, but facing the influence of malware’s code confusion and zero-day attack, aforementioned feature extraction method may cause wrong judge. So, it’s necessary to design an effective technique analysis to prevent malware. In this paper, we use the importance of word from apk, because of code confusion, some malware attackers only rename variables, if using general static analysis wouldn’t judge correctly, then use these importance value to go through our proposed method to generate picture, finally using convolutional neural network to see whether the apk file is malware or not.
Keywords
android; malware; convolutional neural network
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
