SCADA

Supervisory Control and Data Acquisition (SCADA) systems play a vital role in industries that depend on automation and precise operational control, including energy, water management, oil and gas, defense, and railway infrastructure. Because these systems oversee the distribution of essential resources, ensuring their protection against cyber threats is of critical importance.

Data-driven machine learning methods like TNP’s Artificial Intelligence-based Multivariate State Estimation Technique (AI-MSET)—can proactively detect cyber intrusions within SCADA networks. The AI-MSET algorithm learns patterns of normal system behavior and employs the Sequential Probability Ratio Test (SPRT) to identify deviations that may indicate malicious activity, which is independent of dictionary-based intrusions.  

By integrating AI-MSET with TNP’s patented Intelligent Data Preprocessing (IDP) methods, the approach provides a complementary “defense-in-depth” layer to traditional signature-based cybersecurity systems. This framework enhances protection against previously unknown (“zero-day”) attacks by enabling early detection with exceptionally low false-alarm and missed-alarm rates. Furthermore, AI-MSET achieves these results with substantially lower computational overhead compared to conventional neural network–based machine learning models.