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AI and Machine Learning Enabled Security Solutions for Internet of Things (IoT) Based Cyber Physical Systems

Submission Deadline: 30 December 2022 (closed)

Guest Editors

Dr. Muhammad Adil, University at Buffalo, USA.
Dr. Ahmed Farouk, Wilfrid Laurier University and South Valley University, Egypt.


In the recent past, Internet of Things (IoT) based Cyber-Physical System (CPS) has demonstrated remarkable results in many applications such as smart cities, smart healthcare, smart homes, digital industry, etc. Despite its advantages, IoT-based-CPS technology is constituted of resource-limited devices, which need efficient utilization for better operation and results. Therefore, the communication and security aspects should be given prominent importance, while designing or implementing these applications. Following the importance of the aforestated two factors, herein, we will focus on the security concerns of these applications, because they can maintain the trust of clients and other involved stakeholders. Currently, many AI, machine/deep learning-enabled techniques have been used to address this problem and improve the performance of these applications in many aspects such as computation and communication costs. In contrast, the attacker also works restlessly to compromise these techniques and hijack the security of an employed network Keeping in view the existing security challenges, new AI, machine/deep learning-enabled techniques authentication and data preservation schemes should be developed to address the security concerns of IoT-based CPS cost-effectively. To summarize, the central theme of this Special issue is to report novel AI, machine, and deep learning algorithms, theories, protocols, and cryptographic techniques that could be capable to address the security problems of IoT-based CPS safely.


The utmost objective of this special issue is to bring together researchers, developers, and industry experts to address this topic across multiple abstraction levels, ranging from architectural models to provisioning of services, protocols, cryptographic techniques, and interfaces for specific implementation approaches. Furthermore, additional focus will be given to areas related to the role of AI, machine learning, and deep learning algorithms to ensure secure and trustworthy authentication of sensor devices. It aims to present the most important and relevant advances to overcome the challenges related to security, data preservation, Integrity, trustworthiness, safety and efficiency in IoT-based CPS.


Topic of interest includes, but not limited:
• Security & Privacy enhancing cryptographic techniques for IoT based CPS
• Novel security architectures, hardware and software for IoT based CPS
• Vulnerability analysis in the IoT based CPS
• Privacy and data preservation in IoT based CPS
• Context aware machine-learning-based secure data analytics in IoT based CPS
• Interpretable secure Machine learning techniques for IoT based CPS
• Cloud, Edge and Fog computing based secure communication of IoT and CPS
• A lightweight and Homomorphic security protocols for IoT based CPS
• Secure access control mechanism for shared data in IoT based CPS systems
• Intrusion detection and prevention systems for IoT based CPS

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