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Secure Multi-Tenant and Federated Satellite System

Brief Description:

You are required to prepare a research proposal (or business case) for your Dissertation

Solution

RESEARCH PROPOSAL

Abstract:

Conventional satellite communication networks are commonly structured to cater to a solitary user, featuring a single ground station and a single satellite. The growing need for satellite services fuels the advancement of multi-tenant and federated satellite systems. In the context of satellite systems, a multi-tenant system refers to a scenario where multiple tenants collectively utilize the resources of a single satellite. On the other hand, a federated satellite system entails the collaboration of multiple satellites to deliver services to users. The introduction of these novel system architectures presents novel security challenges. In the context of a multi-tenant satellite system, it is conceivable that malevolent tenants may acquire unauthorized access to the data belonging to other tenants. In the context of a federated satellite system, it is conceivable that malevolent entities may exploit weaknesses present in the communication links connecting the satellites, thereby causing disruptions or engaging in unauthorized interception of communications. In order to tackle these security challenges, extensive research is being undertaken to explore a diverse range of techniques. Trusted computing technology can be implemented so only authorized tenants can utilize the satellite resources. A cryptography-based security protocol can be introduced between satellite and ground communications. An intrusion identification system can be deployed over satellite communication to detect malicious activity. Physical attacks can be prevented by using hardware-based security. From the software point of view, several software-defined networks based on various machine learning and deep learning methods can be utilized to protect and address new threats as well as build a secure connection method for satellite communication. As the hardware cannot be altered after deploying the satellite in space, different software-based security protocols can be implemented to develop secure multi-tenant and federated satellite systems. In the present study, machine learning-based hybrid federated learning architectures can be utilized for designing a reliable software-defined satellite networking system.

Introduction:

Satellites are technologically advanced and frequently versatile instruments that are deployed into Earth’s orbit, enabling wireless communications in geolocations that would otherwise be inaccessible and providing their capabilities to terrestrial infrastructure. Satellites are deployed into the Earth’s orbit to expand the wireless communications medium to regions and sites [1]. Satellites are categorized according to the trajectory they adopt, such as low earth orbit, medium earth orbit, and geostationary earth orbit. The rotational speeds of low- and medium-earth orbit satellites are comparatively higher than those of geostationary orbit satellites. This is due to their ability to complete a full orbit around the Earth in approximately 1.5 hours and 5 hours, respectively. The rotation of geostationary satellites is synchronized with the rotation of the Earth. The conventional satellite communication network typically comprises two or three primary components, namely the satellite, ground station, and potential ground users. From a resource utilization perspective, this method of communication exhibits several drawbacks. This obstruction can be resolved through the federated satellite system concept. Within this framework, multiple satellites associated with several organizations can collaborate to enhance the utilization of resources through a mutually beneficial business agreement, such as implementing a pricing model based on usage. The existing cooperation model is further expanded upon through the incorporation of the multi-tenant spacecraft concept. This model refers to a design and operational approach for spacecraft that facilitates the sharing of a single vehicle among multiple organizations. This approach has the potential to yield cost savings and enhance accessibility to space for a range of research, commercial, and governmental purposes. This model is a promising new technology that has the potential to revolutionize the satellite industry. It is still in its early stages of development, but it has the potential to improve efficiency, reduce costs, and increase flexibility for satellite communication users. This technological advancement provides a glimpse into the potential trajectory of satellite communications in the future. Although currently in its early stages, this technology exhibits promising potential for enhancing the accessibility, affordability, and reliability of satellites. As numerous tenants can access the resources of the same spacecraft, it increases the risks of data security breaches, and the performance of the satellite can be affected. These vulnerabilities in the whole system can be reduced by employing a robust encryption algorithm to safeguard sensitive data between the satellite and the ground stations. The system can detect and prevent malicious activities such as unauthorized data extraction. A robust level of security can be guaranteed through the implementation of an advanced network architecture or a security protocol framework that adheres to the standards set by the Consultative Committee for Space Data Systems (CCSDS) for satellite systems. This will additionally contribute to the promotion of both flexibility and stability.

Literature review:

A diverse array of methodologies has been suggested by scholars in academic discourse to safeguard and preserve the confidentiality of satellite systems. In [2], the authors proposed a distributed routine strategy to make predictions about network traffic in LEO satellite networks using machine learning techniques. The implementation of this particular strategy has the potential to enhance both the efficiency and reliability of network routing. The N-tier satellite multicast security protocol proposed by Attila et al. is based on a sign encryption scheme. The use of this protocol is capable of safeguarding the confidentiality and authenticity of data that is transmitted across satellite networks [4]. A recently published study presents a technique aimed at mitigating the impact of jamming attacks on an authentication and key agreement protocol designed for mobile satellite communications [5]. Research-based on blockchain for securing mobile satellite communication has been reported in [6]. In a previous study [7], a novel multicast security protocol was introduced that relies on the utilization of the elliptic curve Pintsov-Vanstone signature. The researchers in reference [8] proposed a novel deep federated learning approach to detect potential threats in satellite communication networks. Researchers have devised a framework centered around protocols that facilitate lightweight authentication and key sharing [9]. Considerable efforts have been made in the realm of network privacy and security, primarily relying on conventionally centralized approaches. Nevertheless, this approach was plagued by a significant computational load. A novel lightweight security model can be implemented in a multitenant and federated satellite model, taking into consideration the aforementioned issues.

Aims and Objectives:

The aims and objectives of the proposed work are shown below:

Aims:

  1. The aim is to design an innovative computer architecture capable of accommodating the security protocols recommended and/or standardized by the Consultative Committee for Space Data Systems (CCSDS). The design of this architecture should prioritize security, efficiency, and scalability.
  2. The objective of this study is to present novel security protocols that can be implemented to safeguard data and resources within a multi-tenant and federated satellite system. It is imperative to develop protocols that possess robustness against a diverse range of attacks, encompassing denial-of-service attacks, man-in-the-middle attacks, and spoofing attacks.
  3. The objective is to create a software-defined satellite networking (SDSN) solution that facilitates the programmability and reconfigurability of the system. The proposed solution ought to be formulated to be versatile and capable of accommodating alterations in both the network topology and traffic patterns.

Objectives:

  1. The objective is to develop and execute a prototype of the suggested computer architecture, which represents a novel form of computer architecture anticipated to yield substantial enhancements in performance compared to current architectures. The implementation of the prototype will utilize Field-Programmable Gate Arrays (FPGAs). The Field-Programmable Gate Arrays (FPGAs) will be employed to emulate the distinct constituents of the envisaged computer architecture, including the central processing unit, storage, and input and output peripherals.
  2. This study aims to analyse and assess the security risks and vulnerabilities associated with a multi-tenant and federated satellite system. Conducting assessments on the efficacy of security protocols in safeguarding against diverse forms of attacks. This study aims to assess the efficacy of security protocols in real-world scenarios, with a focus on their security, efficiency, and usability.
  3. Implement a set of APIs that allow the SDSN to be programmatically controlled. These APIs should be open and standardized so that they can be used by a variety of applications and devices.

Research Question: 

During this ongoing study, several distinct challenges have been identified that must be effectively tackled to ensure the security of multi-tenant and federated satellite systems.

  1. How can the security of a multi-tenant satellite system be ensured in the presence of malicious users?
  2. What are the challenges and solutions for secure communication in a federated satellite system?
  3. In what ways can the security of a multi-tenant satellite system be enhanced through the implementation of software-defined networking (SDN)?
  4. How can the security of a federated satellite system be improved by using blockchain technology?
  5. What are the economic and regulatory factors that need to be taken into account when implementing secure multi-tenant and federated satellite systems?

Research Methods:

Various security protocols can be utilized for multitenant and federated satellite systems. As the satellite requires high power for complex computation and several operations, different lightweight protocols could be utilized. A cryptography-based blockchain methodology produces a secure and tamper-proof environment for federated communication networks. As the federated satellite is utilized by different users, the cryptography-based protocol can be scaled to accommodate a significant number of participants without compromising security issues. Executing the blockchain methodology in a satellite communication system is quite complex which requires a greater amount of power supply and also exhibits relatively high computation burdens. To overcome this trouble a novel security protocol based on hybrid federated learning is proposed for implementing a sophisticated and reliable communication system. Federated transfer learning is a viable approach for transferring knowledge from a pre-trained model on one satellite to another satellite’s model. The weights of the model from the first satellite can be shared with the second satellite. This enables the second satellite to commence its operations with a more refined model compared to the scenario where it would have undergone training from the beginning. The utilization of multitenant satellite systems can render it a valuable asset for the training of machine learning models. The implementation of this approach facilitates the preservation of data privacy, enabling organizations to engage in information sharing and collaborative endeavours. Given its relatively recent development, this technique can be considered more novel in comparison to established methods.

Reference:

[1] N. Koroniotis, N. Moustafa, and J. Slay, “A new Intelligent Satellite Deep Learning Network Forensic framework for smart satellite networks,” Computers and Electrical Engineering, vol. 99, p. 107745, 2022.

[2] Z. Na, Z. Pan, X. Liu, Z. Deng, Z. Gao, and Q. Guo, “Distributed routing strategy based on machine learning for LEO satellite network,” Wireless Communications and Mobile Computing, vol. 2018, 2018.

[3] L. Gunn, P. Smet, E. Arbon and M. D. McDonnell, “Anomaly Detection in Satellite Communications Systems using LSTM Networks,” 2018 Military Communications and Information Systems Conference (MilCIS), Canberra, ACT, Australia, 2018, pp. 1-6, doi: 10.1109/MilCIS.2018.8574109.

[4] A. A. Yavuz, F. Alagz, and E. Anarim, “SAT05-6: NAMEPS: n-tier satellite multicast security protocol based on encryption schemes,” in IEEE Globecom 2006, 2006, pp. 1-6: IEEE.

[5] I. Lasc, R. Dojen, and T. Coffey, “Countering jamming attacks against an authentication and key agreement protocol for mobile satellite communications,” Computers & Electrical Engineering, vol. 37, no. 2, pp. 160-168, 2011.

[6] M. Feng and H. Xu, “MSNET-Blockchain: A new framework for securing mobile satellite communication network,” in 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2019, pp. 1-9: IEEE.

[7] A. A. Yavuz, F. Alagoz and E. Anarim, “A New Satellite Multicast Security Protocol Based on Elliptic Curve Signatures,” 2006 2nd International Conference on Information & Communication Technologies, Damascus, Syria, 2006, pp. 2512-2517, doi: 10.1109/ICTTA.2006.1684802.

[8] S. Salim, N. Moustafa, M. Hassanian, D. Ormod and J. Slay, “Deep Federated Learning-Based Threat Detection Model for Extreme Satellite Communications,” in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2023.3301626.

[9] A. Murtaza, T. Xu, S. Jahanzeb, H. Pirzada, and L. Jianwei, “A lightweight authentication and key sharing protocol for satellite communication,” Int. J. Comput. Commun. Control (2019, in press), 2019.

[10] N. Moustafa et al., “DFSat: Deep Federated Learning for Identifying Cyber Threats in IoT-based Satellite Networks,” IEEE Transactions on Industrial Informatics, 2022.

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