DIMDA: Deep Learning and Image-Based Malware Detection for Android

With the widespread adoption of handheld smartphones, the number of malware targeting them has grown dramatically. Because of the widespread use of cell phones, the quantity of malware has grown dramatically. Because of their ubiquity, android smartphones are the most sought-after targets among smart gadgets. We provide an unique image-based deep learning system for android malware detection in this article. The suggested system predicts if an application is malicious or genuine based on network traffic represented in picture format. The proposed method is tested against 13,533 applications from various banking, gambling, and utilities industries. Our technique is effective, with an accuracy of 98.44% and a recall of 98.30%. It also outperformed conventional machine learning methods.