TIM-8715 v1

Carter, B. T., Bakirtzis, G., Elks, C. R., & Fleming, C. H. (2019). Systems‐theoretic security requirements modeling for cyber‐physical systems. Systems Engineering, 22(5), 411–421.
This journal article presents a security analysis methodology based on the philosophy that requirements related to the system's expected service, or mission, need to be able to mitigate the effects of an exploit. This posture can be greatly aided by the creation of a model that combines system architecture information, its admissible behaviors, and its mission context.

Onumo, A., Ullah-Awan, I., & Cullen, A. (2021). Assessing the moderating effect of security technologies on employees compliance with cybersecurity control procedures. ACM Transactions on Management Information Systems (TMIS), 12(2), 1–29.
This journal article presented research findings that the influence of organizational elements such as leadership on employee security behavior is mediated by espoused cultural values while the impact of employee cognitive belief is moderated by security technologies. The authors make the case that for effective cybersecurity compliance, leaders and policymakers should promote organizational security initiatives that ensure the incorporation of cybersecurity principles and practices into job descriptions, routines, and processes.

Luo, Y., Xiao, Y., Cheng, L., Peng, G., & Yao, D. (Daphne). (2021). Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities. ACM Computing Surveys (CSUR), 54(5), 1–36.
In this journal article, the authors discuss anomaly detection as crucial to ensuring the security of cyber-physical systems (CPS). However, due to the increasing complexity of CPSs and more sophisticated attacks, conventional anomaly detection methods, which face the growing volume of data and need domain-specific knowledge, cannot be directly applied to address these challenges. The authors propose a taxonomy in terms of the type of anomalies, strategies, implementation, and evaluation metrics to understand the essential properties of current methods.