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New Kaspersky Machine Learning for anomaly detection now available

New Kaspersky Machine Learning for anomaly detection now available

Enterprise SecurityIntelligent TechnologyTop Stories

Kaspersky Machine Learning for Anomaly Detection, designed to reveal deviations in production processes at the earliest stage, is now generally available as a commercial product.

The detector is empowered with Machine Learning algorithms that analyse telemetry from machinery sensors. It warns of machine malfunctions by raising alerts as soon as manufacturing process parameters (tags) begin to behave in an unexpected way. Kaspersky Machine Learning for Anomaly Detection (MLAD) provides a feature-rich graphical interface for detailed analysis of anomalies, as well as tools that can integrate the product with existing systems, to deliver alerts to operators’ dashboards.

In industrial settings, it is critical to keep technological process on an optimal path and avoid interruptions of any kind, including: equipment malfunctions, operator errors, or cyberattacks on industrial control systems. If something goes wrong, early detection can prevent disruption and therefore reduce the cost of downtime, the waste of raw materials and the impact of other serious consequences. According to Kaspersky estimates, a 50% reduction in downtime enables annual savings of up to US$1 million for a large power plant or US$2.5 million for an oil refinery.

Kaspersky MLAD’s neural network analyses telemetry in real-time from various sensors used in the production process. It detects minor deviations, such as a change in signals’ dynamics or correlations, and gives alerts before the values reach their thresholds and impact performance. This allows plant operators to take preventive actions. To be able to detect anomalies, the neural network learns the normal behaviour of the machine from historical telemetry data. If a parameter of the production process changes (for example, a new type of raw material is introduced) or a part of the machine is replaced, an operator can re-run the ML trainer to update the neural network. In addition to an ML-based detector, customised diagnostic rules for specific cases can be added at the customer’s request.

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