Autonomous Vehicles With a 6G-Based Intelligent Cybersecurity Model

Algarni, Abdullah; Thayananthan, Vijey

doi:10.1109/access.2023.3244883

Cited by 10 publications

(3 citation statements)

References 44 publications

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“…Several research papers have discussed a number of applications expected to use 6G network. Among the papers that have presented these applications are [22][23][24][25]. Among the expected applications to use the 6G network areindustrial networks, smart healthcare, ecological remote sensing monitoring, holographiccommunication, internet of things, smart railway networks and autonomous vehicles.…”

Section: Applications Of 6gmentioning

confidence: 99%

Robust and Sophisticated Combined-Layered Security: Secured 6G Mobile Network Considerations

Sr,

El-Bendary,

Eltokhy

et al. 2024

Preprint

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Deep learning, machine learning and artificial intelligence have been proposed for enhancing the multimedia processing and security as well as the advanced security tools for 6G networks. The vision of 6G networks refers to linking undersea, terrestrial, and space networks together. This vision involves transferring a massive amount of data over the network. The data hiding based on the deep learning is considered compared to the traditional steganography tools in 6G network, it requires robust and flexible/combined multi- levels of security. This paper presents the proposed vision of 6G security. This paper focuses on security levels on 6G network. The paper proposes a multi-level security system that secures the data without affecting it. The multi-level security system consists of three security levels, which are two encryption techniques and a data hiding technique. The paper carried out several simulation experiments using multi dataset (Mathwork, Yolov8 and others) to evaluate the proposed scenarios and find integration of these techniques that provides the best security performance without affecting the data. The best simulation experiments that provided the best data security performance were the integration between 2D Logistic map, SVD, and Baker Map, respectively. The proposed steganography performs better than the recent published related works and compared with the deep learning based steganography. The proposed combined system provided the better simulation results for image security. The simulation results indicated a perfect match between the original message and the decryption original message after applying the system. The results also indicated that there was no effect on the data and no loss of data.

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Section: Applications Of 6gmentioning

confidence: 99%

Robust and Sophisticated Combined-Layered Security: Secured 6G Mobile Network Considerations

Sr,

El-Bendary,

Eltokhy

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: 2applications Of 6gmentioning

confidence: 99%

Flexible Multi-Layered Security System: Secure 6G Mobile Network Considerations

Sr,

El-Bendary,

Eltokhy

et al. 2023

Preprint

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The vision of 6G networks refers to linking undersea, terrestrial, and space networks together. This vision involves transferring a massive amount of data over the network. 6G network requires very high levels of security. This paper presents the proposed vision of 6G security. This paper focuses on security levels on 6G network. The paper proposes a multi-level security system that secures the data without affecting it. The multi-level security system consists of three security levels, which are two encryption techniques and a data hiding technique. The paper carried outmany simulation experiments to find the best integration of these techniques that provides the best security performance without affecting the data. The best simulation experiments that provided the best data security performance were the integration between 2D Logistic map, SVD, and Baker Map, respectively. This system provided the best simulation results for data security. The simulation results indicated a perfect match between the original message and the decryption original message after applying the system. The results also indicated that there was no effect on the data and no loss of data.

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“…Reduced latency: AI can be used to reduce latency in 6G networks, which is critical in many applications, particularly in real-time video streaming and autonomous driving [9]. • Enhanced security: 6G network security can be improved by using AI, which is vital for preventing cyberattacks [10].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Performance Evaluation of Transfer Learning Algorithms for 6G Wireless Networks

Girelli Consolaro,

Shinde,

Naseh

et al. 2023

Electronics

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The development of the 5G network and the transition to 6G has given rise to multiple challenges for ensuring high-quality and reliable network services. One of these main challenges is the emergent intelligent defined networks (IDN), designed to provide highly efficient connectivity, by merging artificial intelligence (AI) and networking concepts, to ensure distributed intelligence over the entire network. To this end, it will be necessary to develop and implement proper machine learning (ML) algorithms that take into account this new distributed nature of the network to represent increasingly dynamic, adaptable, scalable, and efficient systems. To be able to cope with more stringent service requirements, it is necessary to renew the ML approaches to make them more efficient and faster. Distributed learning (DL) approaches are shown to be effective in enabling the possibility of deploying intelligent nodes in a distributed network. Among several DL approaches, transfer learning (TL) is a valid technique to achieve the new objectives required by emerging networks. Through TL, it is possible to reuse ML models to solve new problems without having to recreate a learning model from scratch. TL, combined with distributed network scenarios, turns out to be one of the key technologies for the advent of this new era of distributed intelligence. The goal of this paper is to analyze TL performance in different networking scenarios through a proper MATLAB implementation.

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Autonomous Vehicles With a 6G-Based Intelligent Cybersecurity Model

Cited by 10 publications

References 44 publications

Robust and Sophisticated Combined-Layered Security: Secured 6G Mobile Network Considerations

Robust and Sophisticated Combined-Layered Security: Secured 6G Mobile Network Considerations

Flexible Multi-Layered Security System: Secure 6G Mobile Network Considerations

Analysis and Performance Evaluation of Transfer Learning Algorithms for 6G Wireless Networks

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