Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3976 2019 2019 Associate Professor, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India Gagan Gagan Associate Professor, Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Srivilliputtur, India P. Chinnasamy Professor, Department of AI & DS, Panimalar Engineering College, Chennai, Tamil Nadu, India S. Kalaimagal Assistant Professor, Computer Science and Engineering, Vishnu Institute of Technology, Andhrapradesh, India Karri Nagaraju Professor and HOD, Department of MCA, B V Raju College Vishnupur, Bhimavaram, Hyderabad, India V. Bhaskara Murthy Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad-500043, Telangana, India Shivanadhuni Spandana Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (DU), Tamilnadu, India M. Rajesh Over uncovered and under-covered areas, satellite communication provides the potential for ubiquity, scalability, and service continuity. However, before these benefits may be fully realized, a number of obstacles need to be overcome. Satellite networks present more difficulties than terrestrial networks in terms of spectrum management, energy consumption, network control, resource management, and network security. The goal of this research is to create a novel way to remote sensing network security modelling by utilizing machine-learning techniques to analyses the security of satellite data. In order to provide an intrusion detection technique for the modern network environment, this study considers data from both terrestrial and satellite networks. Here the remote sensing network security analysis is carried out using quantum federated encryption algorithm and data security has been analysis by quantile regression adversarial convolutional neural networks. Experimental analysis has been carried out in terms of data integrity, latency, random accuracy, QoS, AUC. Proposed technique Data integrity of 93%, LATENCY of 95%, QOS of 96%, random accuracy of 98%, AUC of 92%. 2026 2026 62 70 10.54216/JCIM.170106 https://www.americaspg.com/articleinfo/2/show/3976