One-dimensional convolutional neural networks for Android malware detection

In recent years, malware aims at Android OS has been increasing due to its rapid popularization. Several studies have been conducted for automated malware detection with machine learning approach and reported promising performance. However, they require a large amount of computation when running on...

Full description

Saved in:
Bibliographic Details
Published in2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA) pp. 99 - 102
Main Authors Hasegawa, Chihiro, Iyatomi, Hitoshi
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.03.2018
Subjects
1D convolutional neural network
Androids
Convolution
Convolutional neural networks
Humanoid robots
Machine learning
Malware
malware identification
Smart phones
Online AccessGet full text

Cover

More Information
Summary:In recent years, malware aims at Android OS has been increasing due to its rapid popularization. Several studies have been conducted for automated malware detection with machine learning approach and reported promising performance. However, they require a large amount of computation when running on the client; typically mobile phone and/or similar devices. Thus, problems remain in terms of practicality. In this paper, we propose an accurate and light-weight Android malware detection method. Our method treats very limited part of raw APK (Android application package) file of the target as a short string and analyzes it with one-dimensional convolutional neural network (1-D CNN). We used two different datasets each consisting of 5,000 malwares and 2,000 goodwares. We confirmed our method using only the last 512-1K bytes of APK file achieved 95.40-97.04% in accuracy discriminating their malignancy under the 10-fold cross-validation strategy.
DOI:10.1109/CSPA.2018.8368693