Artificial Intelligence for Trustworthy Industrial Internet of Things

Dear Colleagues,

The Industrial Internet of Things (IIoT) is a sensory structure open to the Internet to supervise and control critical industrial applications. Industrial IoT (IIoT) resolved various industrial platforms’ concerns by promoting self-controlling systems and assuring real-time operations. However, the fast and security-inconsiderate adoption of the IIoT has revealed several security weaknesses to industrial applications. Additionally, IIoT networks produce a vast amount of critical information, and where the corresponding data are not carried and examined securely, a privacy breach can occur.

Unfortunately, traditional security measures, such as data encryption, digital authentication, and access control, are not the optimum choice for IIoT assurance due to network heterogeneity and resource limitations. To utilize the IIoT efficiently, intelligent approaches are demanded to address IIoT security concerns reliably and efficiently.

Machine learning and deep learning (ML/DL) have received significant attention in academic and industrial areas in the last few years. They are considered one of the best computational models that can guarantee intelligent features for IIoT systems.

ML/DL can be extremely beneficial for data profiling, examination, and performance enhancement for the IIoT network components (i.e., sensors, actuators, controllers, and communication means), where the produced information can be used to locate and identify the system weaknesses and potential attacks. Threats and vulnerabilities can be detected at initial phases as the ML/DL-associated solutions have the precedence to discover novel attacks. Subsequently, they can provide advanced security methods for the IIoT context to make it more trustworthy and reliable than before.

Topics of interest in this Special Issue include but are not limited to the following research tracks and technologies:

AI-supported security/privacy methods for IIoT;

IIoT authentication and access control using ML/DL;

Intrusion detection and prevention for IIoT using ML/DL;

Cyberattacks detection and prevention for IIoT using ML/DL;

Privacy breaching detection and prevention for IIoT using ML/DL;

ML/DL threat modeling for IIoT;

Malicious behavior detection for IIoT using ML/DL;

Physical attacks detection in IIoT using ML/DL;

Hardware security using ML/DL for IIoT.

Dr. Mohamed Eldefrawy
Mr. Mahmoud Eldefrawy
Guest Editors

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• Industrial Internet of Things
• IIoT security and privacy
• Artificial Intelligence
• machine learning
• deep learning
• intrusion detection and prevention
• hardware security