The Application of Deep Neural Network to Vulnerability Management on Cyber Physical System – A Systematic Review

Ozioko Frank Ekene and Mba Chioma Juliet
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Ozioko Frank Ekene: Department of Computer Sciences Enugu State University of Science and Technology (ESUT), Enugu, Nigeria
Mba Chioma Juliet: Department of Computer Sciences, Enugu State Polytechnic, Iwollo, Enugu, Nigeria

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 4, 1276-1285

Abstract: Vulnerability management plays a pivotal role in securing Cyber-Physical Systems (CPS) from emerging risks by identifying, assessing, and mitigating potential threats. This paper provides a comprehensive review of existing vulnerability management techniques, highlighting their challenges and limitations when applied to CPS. Specifically, the work examined the role of machine learning, particularly Deep Neural Networks (DNN), in enhancing vulnerability detection and prediction models. DNNs have shown promising results in detecting complex, high-dimensional patterns within large datasets, making them ideal for securing CPS environments. Based on the findings, the paper proposes future research directions that focus on refining DNN-based models to tackle scalability, interpretability, and adaptive security challenges in CPS. By leveraging these advancements, we aim to facilitate more robust, proactive vulnerability management solutions, ultimately contributing to the overall resilience of Cyber-Physical Systems in the face of increasingly sophisticated cyber threats.

Date: 2025
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