Analysis of Dynamic Interference Constraints in Cognitive Radio Cloud Networks

Abstract

To analyze the security performance of a cloud network integrated with a cognitive radio. Cognitive Radio Cloud Network (CRCN) have a resilient potential for dynamic operation for energy saving. Integrating this Cognitive Radio Network (CRN) along with that of a cloud network produces great network exposure, spectrum usage and reduced power consumption. The problem arises for secure transmission and safe storage of huge amount of data in the cloud-based Cognitive Radio (CR) network. This paper, shows a study on preventing the CRCN from some advanced jamming techniques. An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented. A cognitive transmitter uses a pre-trained classifier to predict the current channel status based on recent sensing results and decides whether to transmit or not, and then uses a Generative Adversarial Network (GAN), for generating synthetic data or false data, thus misleading the jammer to sense false data. The transmitter systematically selects when to take wrong actions and adapts the level of defense to mislead the jammer into making prediction errors and consequently increase its throughput. And to use the conventional cryptographic protocols for protecting the stored database in the cloud network from unauthorized users.