Convoy_DTN: A Security Interaction Engine Design For Digital Twin Network

Chen, Meiling; Shao, Jian; Guo, Sixu; Su, Li; Du, Hong

doi:10.1109/gcwkshps52748.2021.9682031

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…The proposed security framework employs a dual-pronged strategy to demonstrate its effectiveness through the NeCS-Car teleoperation benchmark and incorporates enhanced data security at crucial interconnected adapter nodes and an intelligent module. Chen et al [107] also focused on security threats brought by DTN. The authors proposed the DT Protection Function (DTPF) and utilized three security schemes including cross-domain security classification, minimize privacy design, and multidimensional policy validation to ensure security.…”

Section: B Smart Industrymentioning

confidence: 99%

A Comprehensive Survey on Revolutionizing Connectivity Through Artificial Intelligence-Enabled Digital Twin Network in 6G

Sheraz,

Chuah,

Lee

et al. 2024

IEEE Access

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The deployment of 5G has exposed capacity constraints in realizing the key vision of the Internet of Everything (IoE). Therefore, the researchers are exploring potentials of Digital Twin Network (DTN) in wireless networks. DTN is a novel technology to create virtual replicas of physical environment for testing, optimizing, and managing wireless networks. The integration of Artificial Intelligence (AI) and DTN appears to be a promising approach to address communication systems by providing an efficient environment for testing and improving AI models before deployment in real networks for effective network management, optimal resource allocation, and precise behavior prediction. Therefore, AI-enabled DTN in 6G represents a compelling avenue to address multifaceted challenges faced by wireless networks. In this comprehensive work, we offer a holistic survey that delves into the state-of-the-art approaches for AIenabled DTNs in 6G. Firstly, we discuss the evolution of wireless networks and concept of AI-enabled DTN in 6G. Secondly, we discuss the role of AI-enabled DTN in 6G and driving advancements in fundamental components of 6G including resource allocation, caching, data offloading, and data security. Thirdly, we conduct a detailed discussion on key enabling technologies for realizing the capabilities of AI-enabled DTN in 6G. Fourthly, several applications of AI-enabled DTN in 6G are discussed for the practical relevance and significance in various industries such as smart cities, healthcare, and transportation etc. Finally, we provide lessons learned and highlight existing challenges and research directions to embark on further research efforts in the realm of AI-enabled DTN in 6G.INDEX TERMS Digital twin networks (DTNs), 6G, artificial intelligence (AI), caching, resource allocation, data offloading, security, enabling technologies, unmanned aerial vehicle (UAV), mmWave, THz.

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Section: B Smart Industrymentioning

confidence: 99%

A Comprehensive Survey on Revolutionizing Connectivity Through Artificial Intelligence-Enabled Digital Twin Network in 6G

Sheraz,

Chuah,

Lee

et al. 2024

IEEE Access

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“…They also designed the system architecture, analyzed DTN key technologies, investigated the challenges, and pointed out the future development direction of DTN. On that basis, China Mobile subsequently proposed a series of more concrete methods supporting DTN construction [21][22][23]. Yang et al [21] derived a complete flow emulation framework for DTN, which leverages unified ID and deterministic network technology to keep physical network traffic in consistency with that in DTN.…”

Section: Digital Twin Networkmentioning

confidence: 99%

“…Zhu et al [22] proposed a DTN construction method based on knowledge graph, which can represent entity attribute and various topologies according to physical network. Chen et al [23] elaborated a security interaction engine named Digital Twin Protection Function (DTPF) to guarantee the confidentiality, integrity, stability, and non-reputation of messages during DTN policy distribution. DTN also attracts research interests from academia.…”

Section: Digital Twin Networkmentioning

confidence: 99%

Deep Learning Based Traffic Prediction Method for Digital Twin Network

et al. 2023

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Network traffic prediction (NTP) can predict future traffic leveraging historical data, which serves as proactive methods for network resource planning, allocation, and management. Besides, NTP can also be applied for load generation in simulated and emulated as well as digital twin networks (DTNs). This paper focuses on accurately predicting background traffic matrix (TM) of typical local area network (LAN) for traffic synchronization in DTN. A survey is firstly conducted on DTN, conventional model, and deep learning based NTP methods. Then, as the major contribution, a linear feature enhanced convolutional long short-term memory (ConvLSTM) model based NTP method is proposed for LAN. An autoregressive unit is integrated into the ConvLSTM model to improve its linear prediction ability. In addition, this paper further optimizes the proposed model from both spatial and channel-wise dimensions. Particularly, a traffic pattern attention (TPA) block and a squeeze & excitation (SE) block are derived and added to the enhanced ConvLSTM (eConvLSTM) model. Comparative experiments demonstrate that the eConvLSTM model outperforms all the baselines. It can improve the prediction accuracy by reducing the mean square error (MSE) up to 10.6% for one-hop prediction and 16.8% for multi-hops prediction, compared to the legacy CovnLSTM model, with still satisfying the efficiency requirements. The further enhancement of the eConvLSTM model can additionally reduce the MSE about 2.1% for one-hop prediction and 4.2% for multi-hops prediction, with slightly degrading efficiency. The proposed eConvLSTM model based NTP method can play a vital role on DTN traffic synchronization.

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“…Focusing on the type of P2V communication, it is necessary to approach it from a cybersecurity perspective due to the risks and threats associated with an environment of this type, such as risks associated with data, risk to physical devices modeled through DTs, to networks through those that are interconnected or to the network applications that operate on them. As described by Chen et al in [29], P2V communication in NDT architectures must comply with the requirements of data confidentiality and integrity and the stability of the authenticity of policies, as well as the unforgeability based on the authenticity of both parties, physical and digital.…”

Section: Bidirectional Pipelinementioning

confidence: 99%

B5GEMINI: AI-Driven Network Digital Twin

Mozó

Karamchandani

Gómez-Canaval

et al. 2022

Sensors

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Network Digital Twin (NDT) is a new technology that builds on the concept of Digital Twins (DT) to create a virtual representation of the physical objects of a telecommunications network. NDT bridges physical and virtual spaces to enable coordination and synchronization of physical parts while eliminating the need to directly interact with them. There is broad consensus that Artificial Intelligence (AI) and Machine Learning (ML) are among the key enablers to this technology. In this work, we present B5GEMINI, which is an NDT for 5G and beyond networks that makes an extensive use of AI and ML. First, we present the infrastructural and architectural components that support B5GEMINI. Next, we explore four paradigmatic applications where AI/ML can leverage B5GEMINI for building new AI-powered applications. In addition, we identify the main components of the AI ecosystem of B5GEMINI, outlining emerging research trends and identifying the open challenges that must be solved along the way. Finally, we present two relevant use cases in the application of NDTs with an extensive use of ML. The first use case lays in the cybersecurity domain and proposes the use of B5GEMINI to facilitate the design of ML-based attack detectors and the second addresses the design of energy efficient ML components and introduces the modular development of NDTs adopting the Digital Map concept as a novelty.

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Convoy_DTN: A Security Interaction Engine Design For Digital Twin Network

Cited by 6 publications

References 8 publications

A Comprehensive Survey on Revolutionizing Connectivity Through Artificial Intelligence-Enabled Digital Twin Network in 6G

A Comprehensive Survey on Revolutionizing Connectivity Through Artificial Intelligence-Enabled Digital Twin Network in 6G

Deep Learning Based Traffic Prediction Method for Digital Twin Network

B5GEMINI: AI-Driven Network Digital Twin

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