A Taxonomy and Review on Machine Learning Models for Enhancing Security in Wireless Networks

Machine Learning and Data Driven models are being extensively used for physical layer security securing wireless networks. Typically cognitive software defined cognitive networks rely on channel state information (CSI) to be estimated iteratively to monitor the system against potential cyber attacks. Cognitive networks find their applications in military and defence as well with the advent of internet of things and its allied applications in military warfare. Cognitive Radio Networks often share common resources such as bandwidth or spectrum among several users or stations. Due to continued sharing of resources, cognitive networks often come under security attacks, most common of which are jamming and eavesdropping attacks. In this paper, the basics of machine learning assisted software defined networks have been presented with the focus of security awareness through estimates of channel state information. Previous work in the allied domain has been discussed along with their salient features.