SCADA
Additional Information
Supervisory Control and Data Acquisition (SCADA) systems play a vital role in industries that depend on automation and precise operational control, including energy, water and wastewater management, oil and gas, military base SCADA systems, and railway infrastructure (including safety-critical "Switch and Crossing" S&C systems and networks). 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™)— proactively detects malicious intrusion activity within SCADA networks, whether from external hackers or from internal (e.g. disgruntled employee) malicious activity. The AI-MSET™ algorithm learns patterns of normal system dynamic telemetry behavior from all internal Command-and-Control (C&C) hardware, PLCs, RTUs, relays, and networks, then employs the Sequential Probability Ratio Test (SPRT) to identify deviations in activity, even "low and slow" malicious activity "below the noise floor", that may indicate malicious activity, which is independent of dictionary-based intrusion security.
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.
