Security and Privacy Fog-Cloud Assisted Internet of Things Network

Submission Deadline: 31 December 2022 (closed) View: 90

Guest Editors

Dr. Mazin Abed Mohammed, University of Anbar, Iraq.
Prof. Dr. Seifedine Kadry, Norrof University College, Norway.
Assoc. Prof. Dr. Oana Geman, Universitatea Stefan cel Mare din Suceava, Romania.

---

Summary

The fog-cloud assisted Internet of Things (IoT) aware applications are rapidly evolving and transforming people's lives. The Internet of Things (IoT) connects physical devices such as smartphones, smart-watches, sensors, actuators, and thermostats, allowing them to collect and exchange data. According to industry estimates, the number of IoT devices connected will exceed hundreds of billions by 2025. With its ubiquitous connectivity and ultimate functionality, the IoT assisted fog-cloud network introduces security and privacy challenges. The following sections discuss IoT security and privacy concerns. Threats to IoT security do not just exist in cyberspace; they also exist in the physical world. Threats to IoT security do not just exist in cyberspace; they also exist in the physical world. To improve the end-to-end security of IoT devices, both cyberspace and the physical world must be protected. Because of the heterogeneity among devices and service providers, as well as the massive-scale, geographically distributed data in IoT applications, addressing IoT security presents specific challenges. IoT devices may collect sensitive information about users or organizations for privacy reasons. Differential privacy and federated learning are two recent methods that aim to protect privacy in various ways. The application of these methods in the IoT context is both a research and a practical concern.

---

Keywords

• Secure communication fog-cloud network for the IoT
• Threat modelling in the fog-cloud assisted IoT
• Secure architectures for the fog-cloud assisted IoT
• Trust models for the fog-cloud assisted IoT
• Device attestation for the fog-cloud assisted IoT
• Vulnerability analysis in the fog-cloud assisted IoT
• Risk assessment in the fog-cloud assisted IoT
• Intrusion detection for the fog-cloud assisted IoT
• Forensics in the fog-cloud assisted IoT
• Privacy-enhancing techniques for the fog-cloud assisted IoT
• Anonymization techniques the fog-cloud assisted IoT
• Access control in the fog-cloud assisted IoT
• Federated learning in the fog-cloud assisted IoT
• Distributed data analytics, data processing, modeling and training
• Edge and Fog management protocols and policies for workload communication and distribution
• Secure outsourcing computation of fog devices
• Machine and deep learning for solving security in cloud/fog-assisted IoT
• Blockchain technology in cloud/fog-assisted IoT
• AI Methodolgies for Internet of Medical Thingd
• Deep reinforcement learning for complex problems
• Fuzzy system and decision-making for complex problems
• XAI for Internet of Medical Things
• XAI methodologies to detecting emerging medical threats from healthcare data
• XAI and Intelligent Systems for Medical data fusion
• Health Intervention Design, Modeling and Evaluation based on XAI and Intelligent Systems
• Real-time Explainable AI for medical image and data processing
• feature selection for interpretable XAI classification
• XAI and Intelligent Systems for image detection, recognition, and segmentation
• XAI and Intelligent Systems for cancer diagnosis
• Future directions of intelligent XAI medical imaging in healthcare
• XAI and Intelligent Systems for Blockchain Applications
• XAI and Intelligent Systems for Urban Computing and Intelligence
• XAI and Intelligent Systems for Human-Robot Interaction
• XAI and Intelligent Systems for Real-world applications
• XAI and Intelligent Systems for Computer networks and Technology
• XAI and Intelligent Systems for Smart city
• XAI and Intelligent Systems for Security Applications
• XAI and Intelligent Systems for Multi Objectives optimization
• XAI and Intelligent Systems for IoT applications
• XAI and Intelligent Systems for E Learning Systems
• XAI and Intelligent Systems for Engineering complex problems and Applications
• XAI and Intelligent Systems for Cloud and Fog Computing

---

Published Papers

---

Show export options

Select all

Export citation of selected articles as: Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) RIS

• Open Access

  ARTICLE

  A Secure Microgrid Data Storage Strategy with Directed Acyclic Graph Consensus Mechanism

  Jian Shang, Runmin Guan, Wei Wang

  Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2609-2626, 2023, DOI:10.32604/iasc.2023.037694
  （This article belongs to the Special Issue: Security and Privacy Fog-Cloud Assisted Internet of Things Network)
  Abstract The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years. In real-world scenarios, microgrids must store large amounts of data efficiently while also being able to withstand malicious cyberattacks. To meet the high hardware resource requirements, address the vulnerability to network attacks and poor reliability in the traditional centralized data storage schemes, this paper proposes a secure storage management method for microgrid data that considers node trust and directed acyclic graph (DAG) consensus mechanism. Firstly, the microgrid data storage model is designed based on the edge computing… More >

  View

  640

  Download

  1440935