Machine Learning Model in Satellite Data Security Analysis using Remote Sensing Network

Gagan Kumar Koduru^1,*, P. Chinnasamy², S. Kalaimagal³, Karri Nagaraju⁴, V. Bhaskara Murthy⁵, Shivanadhuni Spandana⁶, M. Rajesh⁷

¹Associate Professor, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India

²Associate Professor, Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Srivilliputtur, India

³Professor, Department of AI & DS, Panimalar Engineering College, Chennai, Tamil Nadu, India

⁴Assistant Professor, Computer Science and Engineering, Vishnu Institute of Technology, Andhrapradesh, India

⁵Professor and HOD, Department of MCA, B V Raju College Vishnupur, Bhimavaram, Hyderabad, India

⁶Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad-500043, Telangana, India

⁷Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (DU), Tamilnadu, India

Emails: gagan.koduru@gmail.com; chinnasamyponnusamy@gmail.com; drsivamunikalaimagal@gmail.com; nag2230@gmail.com; murthy.vb@bvrice.edu.in; s.spandana@klh.edu.in; rajesmano@gmail.com

Abstract

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%.

Keywords: Satellite data; Federated encryption; Remote sensing network; Machine learning techniques; Adversarial convolutional