An LSTM (Long Short-Term Memory) neural network is a type of recurrent machine learning model built for sequences and time-series data. Its gated memory cells help it keep useful context over long input windows while discarding irrelevant noise, which makes it effective for forecasting, trend detection, and pattern recognition in data that changes over time.
In cyber security, LSTMs often appear in operational technology, where they may predict load, equipment behavior, or anomalous sensor patterns from telemetry. That makes them useful for defense, but also sensitive to data quality. If an attacker delays, spoofs, or poisons inputs, the model can produce bad forecasts that affect planning or automated responses. Good defenses keep forecasting separate from core control, validate measurements, monitor model drift, and use conservative fallback logic when confidence is low.