Special Issue "Current trends and Advancements for next-generation secure Industrial IoT"

Submission Deadline: 16 February 2021 (closed)
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Guest Editors
Dr. Mahmoud Daneshmand, Stevens Institute of Technology, USA
Prof. Rubén González Crespo, Universidad Internacional de la Rioja (UNIR), Spain
Dr. Qin Xin, University of Faroe Islands, Faroe Islands
Dr. Amrit Mukherjee, Anhui University, China

Summary

The recent advances in wireless communication and ubiquitous computing have made the Internet-of-Things (IoT) a huge paradigm for Industry 4.0 and beyond. The applications are generally based on different technologies that include a broad involvement of Machine Learning (ML) algorithms and other computing techniques. In the research era of 6G projects, drone communication and next-generation Industrial IoT (Nx-IIoT), the advancement in intelligent computations and networking take part as a giant technical model for the government towards smart world implementation.

The involvement of advanced machine learning techniques for these applications from manufacturing raw components to final deployment presents versatile solutions towards its implementation. The role of IIoT and Nx-IIoT connects almost all communication technologies related to Industry 4.0 and beyond on a vast scale which creates a vast opportunity for the researchers and Entrepreneurs to establish their expertise in the competition. The huge volume of big data from IIoT requires smart and secure approaches for analyzing and processing using advanced ML techniques.

The scope of these works in terms of designed architecture, modeling, analyzing and optimizing has been grown exponentially. However, the crucial challenges of IIoT and Nx-IIoT application management, resource sharing, dynamic real-time load balances, secure computations and advanced networking still needs attention.

The SI is targeted to serve the industrial researchers and academia and to present their state-of-art ideas and contributions towards the perspective scope and challenges. Therefore, the SI invites theoretical and practical evaluations of the current trends and advancements in theory and practices to be implemented for secure Nx-IIoT.


Keywords
• Advanced ML techniques for IIoT and Nx-IIoT
• Deep learning architectures for Nx-IIoT
• Optimizations of network for Industry 4.0 and beyond
• Architectures, models and simulations for IIoT and Nx-IIoT
• Security and protocols for Nx-IIoT
• Intelligent middleware design for efficient Nx-IIoT applications
• Cyber and network security algorithms using Nx- IIoT
• Resource allocation and management in Industry 4.0 and beyond
• Security issues, challenges and solutions for IIoT
• Big data and secure cryptography for Nx-IIoT
• DNN based secure communications
• Neuro-fuzzy computations for IIoT and Nx-IIoT
• Software-defined networking for Nx-IIoT
• Cognitive communications and systems for IIoT and Nx-IIoT
• Dynamic clustering for Industry 4.0 and beyond
• Sensor cloud computations for Nx-IIoT
• Ubiquitous sensing and networking for Nx-IIoT
• Nature-inspired secure computational models
• Secure Terahertz communications for Nx-IIoT
• Cloud-RAN for IIoT and Nx-IIoT
• Advanced digital twins computations
• Secure data mining and analytics
• Ambient intelligence for multimedia applications in Nx-IIoT
• Recent trends in IIoT and Nx-IIoT applications

Published Papers
  • Security Threats to Business Information Systems Using NFC Read/Write Mode
  • Abstract Radio Frequency IDentification (RFID) and related technologies such as Near Field Communication (NFC) are becoming essential in industrial contexts thanks to their ability to perform contactless data exchange, either device-to-device or tag-to-device. One of the three main operation modes of NFC, called read/write mode, makes use of the latter type of interaction. It is extensively used in business information systems that make use of NFC tags to provide the end-user with augmented information in one of several available NFC data exchange formats, such as plain text, simple URLs or enriched URLs. Using a wide variety of physical form factors, NFC-compatible… More
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  • MMALE—A Methodology for Malware Analysis in Linux Environments
  • Abstract In a computer environment, an operating system is prone to malware, and even the Linux operating system is not an exception. In recent years, malware has evolved, and attackers have become more qualified compared to a few years ago. Furthermore, Linux-based systems have become more attractive to cybercriminals because of the increasing use of the Linux operating system in web servers and Internet of Things (IoT) devices. Windows is the most employed OS, so most of the research efforts have been focused on its malware protection rather than on other operating systems. As a result, hundreds of research articles, documents,… More
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  • Robust Attack Detection Approach for IIoT Using Ensemble Classifier
  • Abstract Generally, the risks associated with malicious threats are increasing for the Internet of Things (IoT) and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the Industrial Internet of Things (IIoT) network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition, outlier analysis, and speech recognition.… More
  •   Views:387       Downloads:219        Download PDF