Mobile devices within Fifth Generation (5G) networks, typically equipped with Android systems, serve as a bridge to connect digital gadgets such as Global Positioning System (GPS), mobile devices, and wireless routers, which are vital in facilitating end-user communication requirements. However, the security of Android systems has been challenged by the sensitive data involved, leading to vulnerabilities in mobile devices used in 5G networks. These vulnerabilities expose mobile devices to cyber-attacks, primarily resulting from security gaps. Zero-permission apps in Android can exploit these channels to access sensitive information, including user identities, login credentials, and geolocation data. One such attack leverages “zero-permission” sensors like accelerometers and gyroscopes, enabling attackers to gather information about the smartphone’s user. This underscores the importance of fortifying mobile devices against potential future attacks. Our research focuses on a new recurrent neural network prediction model, which has proven highly effective for detecting side-channel attacks (SCAs) in mobile devices in 5G networks. We conducted state-of-the-art comparative studies to validate our experimental approach. The results demonstrate that even a small amount of training data can accurately recognize 37.5% of previously unseen user-typed words. Moreover, our tap detection mechanism achieves a 92% accuracy rate, a crucial factor for text inference. These findings have significant practical implications, as they reinforce mobile device security in 5G networks, enhancing user privacy and data protection.

In the contemporary era, driverless vehicles are a reality due to the proliferation of distributed technologies, sensing technologies, and Machine to Machine (M2M) communications. However, the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy efficient. From existing methods, it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy efficiency. However, the models focus on different aspects separately. There is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence (AI) on autonomous driving and energy efficiency. Towards this end, we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision making. It focuses on both safe driving in highway scenarios and energy efficiency. The deep learning based framework is realized with many models used for localization, path planning at high level, path planning at low level, reinforcement learning, transfer learning, power control, and speed control. With reinforcement learning, state-action-feedback play important role in decision making. Our simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.