Learning Model for Cyber-attack Index Based Virtual Wireless Network Selection

With the availability of different wireless networks in wireless virtualization, dynamic network selection in a given heterogeneous environment is challenging task when there is cyber security and data privacy requirements for wireless users. Selection of low cyber risk network can result in good service experience to the users. Network selection in virtualized wireless environment is determined by various factors such as Quality of Experience (QoE), data loss prevention, security and privacy. In this paper, we propose a learning model for dynamic network selection based on cyber-attack index (CI) value of networks. We have develop a recommendation system which recommends user to select the most secure network with least CI value. A mathematical model based on least squares and convex optimization is presented which predicts the CI of network with goal of maximizing the number of wireless users/subscribers. Numerical results show that the CI based recommendation system outperforms the traditional prediction based systems. Furthermore, we compare our approach with existing approaches and found that the proposed approach results in better performance in terms maximizing the number of wireless users/subscribers and better services to them.