A Novel Approach for Android Malware Detection and Classification using Convolutional Neural Networks

Ahmed Lekssays, Bouchaib Falah, Sameer Abufardeh


Malicious software or malware has been growing exponentially in the last decades according to antiviruses vendors. The growth of malware is due to advanced techniques that malware authors are using to evade detection. Hence, the traditional methods that antiviruses vendors deploy are insufficient in protecting people’s digital lives. In this work, an attempt is made to address the problem of mobile malware detection and classification based on a new approach to android mobile applications that uses Convolutional Neural Networks (CNN). The paper suggests a static analysis method that helps in malware detection using malware visualization. In our approach, first, we convert android applications in APK format into gray-scale images. Since malware from the same family has shared patterns, we then designed a machine learning model to classify Android applications as malware or benign based on pattern recognition. The dataset used in this research is a combination of self-made datasets that used public APIs to scan the APK files downloaded from open sources on the internet, and a research dataset provided by the University of New Brunswick, Canada. Using our proposed solution, we achieved an 84.9% accuracy in detecting mobile malware.


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