Next-Generation Cybersecurity Solutions for Cyber-Physical Systems

Dear Colleagues,

In an era where technology intersects with every aspect of daily life, the security of cyber-physical systems (CPS) has become paramount. These systems, which integrate computing, networking, and physical processes, are the backbone of critical infrastructure, manufacturing, healthcare, and more. The Special Issue, entitled 'Next-Generation Cybersecurity Solutions for Cyber-Physical Systems', seeks to explore innovative security strategies that leverage the latest advancements in technology to protect these essential systems from evolving threats. Technologies such as blockchain, federated learning (FL), and quantum machine learning (QML) are at the forefront of this exploration, offering new ways to enhance the robustness and adaptivity of security solutions. This Special Issue will highlight research that pushes the boundaries of traditional cybersecurity to offer solutions capable of withstanding sophisticated cyber threats and ensuring the resilience of CPS. Perfectly aligned with the scope of Automation, this Special Issue focuses on how advanced technologies can automate key security tasks, enhancing the protection mechanisms within CPS. By integrating cutting-edge technologies like blockchain and FL into automated security solutions, we contribute to the automation and optimization of critical sectors, ensuring that CPS remain both resilient and reliable in facing modern challenges. This approach not only secures systems but also advances the broader field of automation by integrating state-of-the-art security practices into the very fabric of automated processes.

For this Special Issue, the topics of interest could include a wide range of areas that focus on integrating cutting-edge technologies to enhance the security of cyber-physical systems. Below are examples of suggested topics that would fit well with the theme of the Special Issue:

• AI-enhanced security protocols in CPS;
• Blockchain for secure CPS communications;
• Blockchain-enabled identity and access management in CPS;
• Quantum-resistant cryptography in CPS;
• Quantum machine learning for enhanced threat prediction in CPS;
• Federated learning for distributed security in CPS;
• Privacy preservation through federated learning in CPS;
• Machine learning-based anomaly detection in CPS;
• Secure IoT integration in CPS;
• Automated defense mechanisms in CPS;
• Automated and AI-driven security solutions in CPS.

Dr. Abbas Yazdinejad
Dr. Quanyan Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Automation is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• cyber-physical systems
• next-generation cybersecurity
• artificial intelligence
• blockchain
• quantum computing
• federated learning
• threat intelligence
• quantum machine learning
• anomaly detection