Attention-based Malware Detection of Android Applications

The explosive rise of malware poses risks to Android developers and organization regarding security lapses and monetary losses. The dynamic nature, changing complexity and behavior over time, and increasing velocity and volume make it challenging for the malware protection community to provide a robust and reliable protection system. Due to these characteristics, conventional Android malware detection techniques, such as signature-based and battery-monitoring, cannot detect futuristic malware. Current research exploiting deep learning methods shows excellent performance compared to conventional and machine learning methods. However, the majority of the techniques are proposed for only binary classification. These classification models are tested on customized datasets. They do not provide the model's effectiveness in terms of generalization, as the model's accuracy might be good for some malware classes. Hence, providing a practical, robust, stable, and reliable malware model is still an open issue. Therefore, in this work, we propose an Attention-based deep learning model to detect categorical malware classes. The attention-based deep learning mechanism learns the malicious behavior of target classes. The attention mechanism filters and extracts the relevant information more effectively by focusing on the specific keywords in a target sample.