Attacks that are capable of disrupting operating technologies are the most dangerous for an industrial facility because they could reduce the quality of the industrial process and even cause irreversible damage to equipment, thereby leading to enormous financial losses and a damaged reputation.
A facility may have a large number of processes, and therefore a malfunction could go unnoticed for a long time. During an attack, hackers usually try to conceal their malicious impact as long as possible. Under these types of conditions, traditional solutions are insufficient for protecting an industrial environment against threats aimed at the process infrastructure.
Kaspersky Machine Learning for Anomaly Detection (Kaspersky MLAD) is an innovative system that employs a neural network to simultaneously monitor a large number of telemetry indicators and detect anomalies in the operation of cyber-physical systems comprising state-of-the-art industrial facilities.