Abstract
Predicting attacks in Android Malware (AM) devices within recommender systems-based IoT is challenging. A novel framework is presented in this study for AM Detection (AMD) using BERT Ensemble (MBR) and MobileNetV2. The MBR model uses a threat analysis technique to assess Android apps by using a subset of 100 permissions from 329 Android application-based permissions, together with a refined feature set. Using MCADS, DroidRL, CNN, FAGnet, GAN, and FEDriod, the MBR model performs exceptionally well, achieving 98% accuracy, 96% precision, 98% recall, 97% F1-score, and a log loss of 0.058. By leveraging their strengths, the MBR model introduces significant innovation. By using ensemble methods on static data, the MBR framework not only provides a reliable malware detection solution but also presents a novel strategy. This research highlights the potential for significant applications in this dynamic and evolving field by addressing user privacy and system security issues, despite the growing Android malware risks in IoT.