Secure Cyber Physical Systems: Machine Learning and Cryptography

Dear Colleagues,

We invite you to contribute to our Topic on “Secure Cyber Physical Systems: Machine Learning and Cryptography”. This Topic will bring together cutting-edge research and advancements in leveraging cryptographic techniques and machine learning approaches to ensure the security and privacy of cyber–physical systems (CPSs). With CPS playing a vital role in industries such as healthcare, manufacturing, transportation, and energy, addressing their unique security challenges is crucial for safeguarding critical infrastructure and data.

In this Topic, original research articles, reviews, and case studies are welcome. Research areas may include (but are not limited to) the following:

1. Security vulnerabilities and threat modeling in CPS;
2. Applications of cryptographic algorithms for secure CPS communication and data integrity;
3. Machine learning approaches for intrusion and anomaly detection in CPS;
4. Privacy-preserving machine learning techniques for CPS security;
5. Emerging trends in blockchain and distributed ledger technologies for CPS;
6. Advances in post-quantum cryptography for secure CPS;
7. Case studies on machine learning and cryptography applications in CPS sectors (e.g., autonomous vehicles, medical devices, industrial IoT);
8. Artificial general intelligence approaches/reinforcement learning for CPS cybersecurity.

We look forward to receiving your contributions, which will advance the field and create secure, resilient, and intelligent CPS systems.

Dr. Heena Rathore
Dr. Henry Griffith
Dr. Yuchen Jiang
Topic Editors