Attacks targeting operational technologies (OT) are the most dangerous for industrial facilities because they can disrupt the technological process and do irreversible damage to equipment, resulting in major financial and reputational losses. Some attacks on OTs do not come from inside the digital environment (reflashing the controller or spoofing sensor readings), but are purely physical (shutting off a valve, removing a sensor, or attaching a false sensor). There are so many processes inside an enterprise that the harmful effects can go unnoticed for a long time, especially since the attackers usually try to hide their malicious actions. In such conditions, traditional solutions are unable to protect the industrial environment from threats aimed at technological infrastructure.
Kaspersky Machine Learning for Anomaly Detection (Kaspersky MLAD) is an innovative system that uses a neural network to simultaneously monitor a wide range of telemetry data and identify anomalies in the operation of cyber-physical systems, which is what modern industrial facilities are.