Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control

• URL: http://arxiv.org/abs/2204.01950v1
• Date: Tue, 5 Apr 2022 03:04:02 GMT
• Title: Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control
• Authors: Jithin Jagannath, Keyvan Ramezanpour, Anu Jagannath
• Abstract summary: Digital twin (DT) technologies have emerged as a solution for real-time data-driven modeling of cyber physical systems. We establish a conceptual layered architecture for a DT framework with decentralized implementation on cloud computing. We discuss the significance of DT in lowering the risk of development and deployment of innovative technologies on existing system.
• Score: 3.1798318618973362
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Digital twin (DT) technologies have emerged as a solution for real-time data-driven modeling of cyber physical systems (CPS) using the vast amount of data available by Internet of Things (IoT) networks. In this position paper, we elucidate unique characteristics and capabilities of a DT framework that enables realization of such promises as online learning of a physical environment, real-time monitoring of assets, Monte Carlo heuristic search for predictive prevention, on-policy, and off-policy reinforcement learning in real-time. We establish a conceptual layered architecture for a DT framework with decentralized implementation on cloud computing and enabled by artificial intelligence (AI) services for modeling, event detection, and decision-making processes. The DT framework separates the control functions, deployed as a system of logically centralized process, from the physical devices under control, much like software-defined networking (SDN) in fifth generation (5G) wireless networks. We discuss the moment of the DT framework in facilitating implementation of network-based control processes and its implications for critical infrastructure. To clarify the significance of DT in lowering the risk of development and deployment of innovative technologies on existing system, we discuss the application of implementing zero trust architecture (ZTA) as a necessary security framework in future data-driven communication networks.

Related papers

• Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks [19.697853431302768]
  Digital twins (DTs) embody real-time monitoring, predictive, and enhanced decision-making capabilities. This study investigates the security challenges in distributed network DT systems, which potentially undermine the reliability of subsequent network applications.
  arXiv  Detail & Related papers  (2024-07-02T03:32:09Z)
• A Cutting-Edge Deep Learning Method For Enhancing IoT Security [0.0]
  This paper proposes an innovative design of the Internet of Things (IoT) Environment Intrusion Detection System (or IDS) using Deep Learning-integrated Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Our model, based on the CICIDS 2017 dataset, achieved an accuracy of 99.52% in classifying network traffic as either benign or malicious.
  arXiv  Detail & Related papers  (2024-06-18T08:42:51Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Multi-Tier Computing-Enabled Digital Twin in 6G Networks [50.236861239246835]
  In Industry 4.0, industries such as manufacturing, automotive, and healthcare are rapidly adopting DT-based development. The main challenges to date have been the high demands on communication and computing resources, as well as privacy and security concerns. To achieve low latency and high security services in the emerging DT, multi-tier computing has been proposed by combining edge/fog computing and cloud computing.
  arXiv  Detail & Related papers  (2023-12-28T13:02:53Z)
• Digital Twin-Native AI-Driven Service Architecture for Industrial Networks [2.2924151077053407]
  We propose a DT-native AI-driven service architecture in support of the concept of IoT networks. Within the proposed DT-native architecture, we implement a TCP-based data flow pipeline and a Reinforcement Learning (RL)-based learner model.
  arXiv  Detail & Related papers  (2023-11-24T14:56:13Z)
• Bringing AI to the edge: A formal M&S specification to deploy effective IoT architectures [0.0]
  The Internet of Things is transforming our society, providing new services that improve the quality of life and resource management. These applications are based on ubiquitous networks of multiple distributed devices, with limited computing resources and power. New architectures such as fog computing are emerging to bring computing infrastructure closer to data sources.
  arXiv  Detail & Related papers  (2023-05-11T21:29:58Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Intelligent Zero Trust Architecture for 5G/6G Tactical Networks: Principles, Challenges, and the Role of Machine Learning [4.314956204483074]
  We highlight the challenges and introduce the concept of an intelligent zero trust architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted components. This paper presents the architectural design of an i-ZTA upon which modern artificial intelligence (AI) algorithms can be developed to provide information security in untrusted networks.
  arXiv  Detail & Related papers  (2021-05-04T13:14:29Z)
• A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning [115.75967665222635]
  Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
  arXiv  Detail & Related papers  (2020-09-13T14:53:01Z)
• A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service [68.84245063902908]
  This paper introduces a novel distributed architecture for deep-learning-as-a-service. It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
  arXiv  Detail & Related papers  (2020-03-30T15:12:03Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.