Guest Editors
Dr. Khalid Haseeb, Islamia College Peshawar, Pakistan.
Dr. Tanzila Saba, Prince Sultan University, Saudi Arabia.
Dr. Amjad Rehman Khan, Prince Sultan University, Saudi Arabia.
Summary
Cyber-Physical Systems are very important frontrunners in the digital and autonomous transformation pathway. Over the years, these systems have developed into more sophisticated and smart solutions for the industrial, healthcare, transportation, and energy sectors. When it comes to offering cybersecurity solutions to address the existing privacy threats in the system, the most important governing factor is the difficulty in addressing the predictive nature of the system due to its heterogeneous components. In order to overcome this drawback, Artificial Intelligence (AI) and predictive algorithms are very helpful. The major advantage of such AI-based predictive intelligence systems is the avoidance of threats that occur on the zero-day due to their varied and undeterminable misbehaviour. The predictive AI-based cybersecurity system handles such a task by generating an automatic attack pattern in incoming heterogeneous datasets with the help of Machine Learning (ML) algorithms. Later, various decision-making approaches are employed to classify the suspicious attacks or threats and come up with an optimal solution. Human-Machine interaction, AI, and game theory approach collectively represent the resources in developing such an intelligent Cyber-Physical System protection model in order to avoid false-positive results.
One of the most promising research directions in this field includes the use of a brain-type distributed control system in a Cyber-Physical System environment. Such a distributed system architecture will ensure cybersecurity in networks operating based on fog, radio, and optical networks. Such methods have proven to decrease the loss of data packets, latency requirements, and probability of network blocking considerably. Also, such a model can be deployed to achieve improved accuracy in the detection of nodes that contain malicious and dangerous Cyber-Physical Systems. The authentication and authorization processes in most of the mainstream cybersecurity systems follow a combined approach that employs AI and security provisioning. Such systems offer excellent protection against privacy threats in wireless communication in the Internet of Things (IoT). Another major area of research is the protection of network slices against privacy threats and denial-of-service type of attacks. Also, integration of bi-directional recurring deep neural networks and propagative distributed belief protects Cyber-Physical Systems against GPS spoofing attacks. With the increase in technological systems, it is equally important to protect these systems against malicious attacks and privacy threats. Novel system architectures and in-built algorithms can be developed in order to help prove these Cyber-Physical Systems against attacks.
Keywords
Topics of interest include but are not limited to:
•Innovative design and verification methods for ensuring privacy in Cyber-Physical Systems.
•Integration of game theory and big data models for the development of an intelligent threat prediction model in smart transportation networks.
•Design of context-aware systems for predictive and smart management of Cyber-Physical Systems.
•Ensuring the security of various layers of a Cyber-Physical System using predictive intelligence and Deep Reinforcement Learning.
•Innovative ML tool based on IDS and IPS mechanisms for cybersecurity in critical Cyber-Physical Systems.
•Modelling and simulation of the predictive AI system behaviour in cases of security alerts in a Cyber-Physical System.
•Novel tools and technologies for authenticating and accessing data sources in a Cyber-Physical System.
•Methods for data correlation and collection for protection in Cyber-physical systems through the limitation of pseudonyms.
•Innovative tools and technologies to avoid false-positive results in Cyber-Physical System protection tools.
•Methods for treating virtual emotion barrier in Cyber-Physical Systems in smart manufacturing systems.
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