Current Trends in Cyber-Physical Systems Security and Optimization

Recent advancements in the field of Cyber-Physical Systems (CPS) have seen a significant shift towards enhancing security through innovative anomaly detection methods and optimizing system performance through novel instrumentation techniques. The focus has been on leveraging AI and advanced compiler-based tools to automate and streamline processes that were previously manual and resource-intensive. This includes the extraction of physical invariants from CPS documentation using generative AI models, which not only reduces costs but also improves scalability. Additionally, there is a growing emphasis on developing secure estimators that can handle sensor threats in real-world applications, as well as frameworks for manipulating bitstreams in FPGAs to address security vulnerabilities.

In the realm of anomaly detection, adaptive methods are gaining traction for their ability to process data quickly and adapt models in real-time, making them effective against evolving cyberattacks. These methods are particularly noteworthy for their integration of AI models with side-channel power analysis to detect hardware trojans in ICs, a critical area for securing CPS.

Notable developments include:

• The use of LLM-assisted physical invariant extraction for anomaly detection in CPS, which achieves high precision while minimizing false alarms.
• A secure estimator with a Gaussian Bernoulli Mixture Model that improves detection and estimation performance in the presence of sensor threats.
• A comprehensive framework for manipulating bitstreams in FPGAs, highlighting the urgency of developing effective countermeasures against bitstream manipulation attacks.
• An AI-enabled side-channel power analysis method for hardware trojan detection, demonstrating higher accuracy compared to baseline models.

These innovations collectively push the boundaries of CPS security and optimization, offering robust solutions to current and emerging challenges in the field.