Effectiveness of Video-Classification in Android Malware Detection Through API-Streams and CNN-LSTM Autoencoders

Abstract

The outbreak of the COVID-19 pandemic has forced worldwide employees to massive use of their mobile devices to access corporate systems. This new scenario has made mobile devices more susceptible to malicious applications, which are yearly developed to conduct several hostile activities. Concerned about this fact, many Deep Learning (DL) based solutions have been proposed, in the last decade, by considering both static and dynamic approaches. However, static solutions are adversely affected by obfuscation techniques and polymorphic applications, while dynamic ones cannot reduce the damages caused during applications execution. To this purpose, the following paper aims to propose a novel approach called API-Streams to minimize damages at Run-time. Therefore, we investigate several Video-Classification tasks through CNN-LSTM Autoencoders (CNN-LSTM-AEs). More precisely, we combine the capability of AEs in finding compact features with the classification abilities of Deep Neural Networks (DNNs), and we show that the proposed approach achieves an average accuracy of 98% in the presence of several unbalanced training datasets. Finally, we use the t-Stochastic Neighbor Embedded (t-SNE) representation technique to investigate the abilities of the employed AE to cluster data into their respective classes by limiting their overlapping.