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This article proposes a structured protocol based on a nonlinear algorithm to prevent cyber-enabled nuclear terrorism in nuclear power plants (NPPs). The authors analyze potential threat vectors in NPP instrumentation and control (I&C) systems and model malicious actions via nonlinear dynamic systems, enabling detection and mitigation strategies tailored to cyber-nuclear threats. They construct a defense protocol that adapts dynamically according to the system’s state, thereby strengthening resilience. This work is highly relevant today: as nuclear facilities digitize and face advanced cyber-physical threats, such algorithmic and adaptive security frameworks are critical to safeguarding critical nuclear infrastructure. The protocol offers a practical model for regulators, plant operators, and cybersecurity professionals to integrate cyber-defense in nuclear operations, contributing to broader efforts in nuclear security and counter-terrorism.This article represents a valuable and innovative contribution to the field of nuclear cybersecurity. By combining nonlinear dynamic modeling with protocol design, the authors bring a rigorous, systems-engineering approach to addressing cyber threats in NPPs, making this work significant for both academic and practitioner communities.Strengths: The paper’s major strength lies in its novel use of a nonlinear algorithm to model threat dynamics in nuclear control systems, which allows for more realistic simulation of cyber-attack scenarios. It bridges the gap between cybersecurity theory and nuclear engineering practice, proposing a defensible protocol rather than just risk assessment. The authors leverage system-theoretic thinking, making their model adaptive and potentially more resilient to evolving threats. Weaknesses: The article may have limited empirical validation: while the model and protocol are theoretically robust, there is no indication of large-scale implementation or real-world testing in operational NPP environments (at least in the published work). Also, reliance on nonlinear modeling may introduce complexity that could hinder practical adoption if operators lack the mathematical or systems background. Compared to more standard cyber risk methodologies (e.g., probabilistic risk assessment, Markov decision processes), the nonlinear approach might be more difficult to standardize or certify under regulatory frameworks. Unique Contributions: Its systemic, nonlinear-protocol design distinguishes it from literature that relies mostly on static risk assessment or linear threat models. In comparison with other works — such as cyber-attack response planning via Markov decision processes for NPPs Bohrium — this article offers a more dynamic defense strategy potentially more suited for adaptive, persistent threats.