R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections
TonTon Hsien-De Huang, and Hung-Yu Kao

TL;DR
This paper introduces R2-D2, a CNN-based Android malware detection system that converts app bytecode into color images for automatic feature learning, reducing manual feature engineering and improving detection accuracy.
Contribution
The paper presents a novel color-inspired CNN approach that transforms bytecode into images for malware detection, eliminating the need for pre-extracted features.
Findings
High accuracy in malware detection
Effective automatic feature extraction from bytecode images
Large dataset of 2 million apps used for training and testing
Abstract
The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware. The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware. In order to reduce the manpower of feature engineering prior to the condition of not to extract pre-selected features, we have developed a coloR-inspired convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2) system. The system can convert the bytecode of classes.dex from Android archive file to rgb color code and store it as a color image with fixed size. The color image is input to the convolutional neural network for…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Network Security and Intrusion Detection · Digital and Cyber Forensics
MethodsConvolution
