Kaspersky Machine Learning for Anomaly Detection predicts downtime in industrial environments. This allows deviations in the production processes to be identified at an early stage and downtimes to be reduced.
The solution is equipped with machine learning algorithms that analyze the telemetry of machine sensors. It warns of machine errors by triggering warnings as soon as the parameters of the manufacturing process (tags) behave unexpectedly. Kaspersky Machine Learning for Anomaly Detection also offers a feature-rich visual interface for detailed analysis of the anomalies, as well as tools that allow the product to be integrated into existing systems to send alerts to users' dashboards.
Industrial plants usually do not tolerate failures
A smooth process is essential in industrial environments; Equipment malfunctions, operating errors or cyber attacks on industrial control systems must be avoided. However, if the worst comes to the worst, early detection can help reduce the cost of downtime, waste of raw materials, and the effects of other serious consequences. Kaspersky estimates that reducing downtime by 50 percent can save up to $ 1 million annually for a large power plant or $ 2,5 million for an oil refinery.
Neural network for machine learning
Kaspersky Machine Learning for Anomaly Detection's neural network analyzes the telemetry of various sensors used in the production process in real time. The solution detects even minor deviations, such as a change in signal dynamics or signal correlations, and notifies users before the values reach their limits and affect performance. This enables system operators to take preventive measures. In order to be able to recognize anomalies, the neural network learns the normal behavior of the machine from historical telemetry data. Should a parameter of the production process change, for example because a new type of raw material is introduced or a part of the machine is replaced, an operator can carry out the machine learning training again in order to update the neural network. In addition to a machine-learning-based detector, individual diagnosis rules can also be added for specific cases.
Kaspersky Machine Learning for Anomaly Detection
Kaspersky Machine Learning for Anomaly Detection can be used in the infrastructure of the existing system and does not require the installation of additional sensors. To receive data and report the anomalies, Kaspersky Machine Learning for Anomaly Detection connects to industrial control systems such as SCADA. Alternatively, the solution can be integrated into Kaspersky Industrial CyberSecurity for Networks. The product natively supports common protocols such as OPC UA, MQTT, AMQP and REST, which means that it can be used on systems with different devices.
Kaspersky Machine Learning for Anomaly Detection Console (Image: Kaspersky).
Kaspersky Machine Learning for Anomaly Detection provides a visual interface for analyzing detected anomalies. Based on the visualized diagrams of all monitored processes, an expert can see what went wrong when and in which part of the system.
Indispensable tool for smooth production
"Advanced machine learning algorithms and the ability to adapt to specific industrial processes make Kaspersky Machine Learning for Anomaly Detection an indispensable tool for smooth production," said Andrey Lavrentyev, Head of Technology Research Department at Kaspersky. “The solution complements monitoring services and machine operator expertise with the ability to detect anomalies in a complex environment. Regardless of what's causing the discrepancies, early warning can prevent downtime, equipment failure, and disasters. We have been developing the technology for several years and are pleased to announce today the general availability of the full-fledged product so customers can take advantage of these benefits.”
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