INTELLIGENT BRANDS // Enterprise Security
POWERED BY
New Kaspersky Machine Learning for anomaly detection now available
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 .
“ Advanced Machine Learning algorithms and the ability to adapt to particular industrial processes make Kaspersky Machine Learning for Anomaly Detection an essential tool to ensure smooth production .
It complements monitoring systems and machine operators ’ expertise with the ability to detect anomalies in a complex environment . No matter what causes the deviations , the downtime , equipment breakdowns and disasters can be prevented thanks to early alerts . We have been developing the technology for several years and today we ’ re happy to announce the general availability of the fully- fledged product to help customers achieve these benefits ,” said Andrey Lavrentyev , Head , Technology Research Department , Kaspersky . p
60 INTELLIGENTCIO AFRICA www . intelligentcio . com