One-dimensional convolutional neural networks for Android malware detection

• Published in: 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA) pp. 99 - 102
• Main Authors: Hasegawa, Chihiro, Iyatomi, Hitoshi
• Format: Conference Proceeding
• Language: English; 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
• DOI: 10.1109/CSPA.2018.8368693

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.