The field of cyber-physical security is moving towards the development of more robust and resilient systems, with a focus on anomaly detection, causal analysis, and privacy-aware frameworks. Recent research has highlighted the importance of integrating causal inference theory with digital twin modeling to improve anomaly detection and root cause analysis. Additionally, the use of neurosymbolic causal analysis and hybrid quantum computing is being explored to enhance the security and trustworthiness of cyber-physical systems. Noteworthy papers in this area include: Causal Digital Twins for Cyber-Physical Security, which proposes a novel framework for robust anomaly detection in industrial control systems. CausalTrace, a neurosymbolic causal analysis agent for smart manufacturing, which performs data-driven causal analysis and supports real-time operator interaction. Privacy-Aware Framework of Robust Malware Detection in Indoor Robots, which leverages hybrid quantum computing and deep neural networks to counter DoS threats while preserving privacy information.