Adaptive AI-driven cyber threat detection system for U.S. critical infrastructure protection

More and more complex cyberattacks targeting America’s essential infrastructure endanger the nation’s safety, financial health and people’s safety. A lot of the time, rule-based cybersecurity does not notice new and growing dangers in real-time, leaving major systems exposed. Our research introduces an AI cyber threat detection framework based on using autoencoders and LSTM networks that improves both accuracy and speed in finding threats. Continual learning and reinforcement learning are part of the system so it can adapt to new threats in real time. Tests of our system on data from replay SCADA logs and NSL-KDD show very effective detection. The model’s dependability is confirmed by metrics such as precision, recall and F1-score and both its edge and cloud deployments allow for both speed and support for a growing number of devices. One solution to explain how AI reaches its decisions is to use SHAP and LIME. For now, we have applied our results to simulated situations, but our next step is to use the system in real places. The research introduced a resilient, flexible and easily explainable artificial intelligence method to make national critical infrastructure more secure.