Volume 17 , Issue 1 , PP: 62-70, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Gagan Kumar Koduru 1 * , P. Chinnasamy 2 , S. Kalaimagal 3 , Karri Nagaraju 4 , V. Bhaskara Murthy 5 , Shivanadhuni Spandana 6 , M. Rajesh 7
Doi: https://doi.org/10.54216/JCIM.170106
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%.
Satellite data , Federated encryption , Remote sensing network , Machine learning techniques , Adversarial convolutional
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