ABSTRACT
Mobile virtual Network Operators (MVNOs) and Wireless Infrastructure Provider (WIPs) make profits from centralized management and interactive resource sharing to end users, which enhances the spectrum efficiency and scalability of Internet of Things (IoT) applications in the wireless virtualization environment. But due to the large number of wireless devices and with the fast growth in various wireless services and different applications, IoT devices are vulnerable to possible cyber-attacks and there is a need to prevent these attacks. The latest challenges faced by IoT devices are insufficient privacy protection, lack of IoT device management and lack of physical hardening. Connectivity of the massive number of Internet of Things devices will increase the possibility of malware attacks. At the same time, it enables AI/ML based analytics to offer security solutions based on exploiting the behavioral patterns of the local environment by sharing this metadata with cloud operators. In this chapter, we propose an artificial intelligence-based two-stage malware detection empowered by software-defined technology. It flexibly captures networks’ metadata with a global view and detects malware and attacks intelligently. We leverage neural network learning models to investigate and to classify the attacks, and extract the features of the attacks. Use the data to prevent IoT device privacy and manage the wireless resources intelligently so that the payoff of the MVNOs and WIPs and the security of the IoT devices increases.
