AI-enhanced Cybersecurity in Fluid Dynamics Simulation Software

This study presents a full framework for merging artificial intelligence (AI) with simulation software to increase cybersecurity defenses. This suggested system, which integrates powerful machine learning algorithms with secure protocols, enables real-time identification of threats and enhanced protection against cyberattacks, particularly built for fluid dynamics simulation settings. The architecture employs AI models like Random Forest and Long Short-Term Memory (LSTM), which have been enhanced via hyperparameters tuning and trained on a dataset that encompasses both simulated and real-world cyber events. The findings demonstrate considerable success in key security parameters, including a ${3 5 \%}$ spike in threat detection rates and a ${2 0 \%}$ decline in system vulnerabilities, when compared to traditional cybersecurity measures. The implementation of these AI-driven protocols increased the software’s reliability and convenience of use for engineers and researchers. Although various constraints were observed, including computational resource needs, the whole system delivers a scalable and effective solution for the protection of high-value simulation settings from emerging cyber threats. Future research will be targeted at extending the dataset and integrating more complex AI technologies to further increase security resilience.