Výskumníci z Oddelenia senzorových systémov Výskumného centra UNIZA v spolupráci s Materiálovotechnologickou fakultou STU v Trnave dosiahli významný pokrok v oblasti bezpečnosti priemyselných sietí.
In a recently published paper titled „Machine Learning-Based Detection of Anomalies, Intrusions, and Attacks in Industrial Control Systems“ published in IEEE Access, authors presented an innovative approach for detecting anomalies and attacks in industrial control systems (ICS) using machine learning. The researchers confirmed that machine learning methods can effectively identify anomalies in their network communications, helping to detect and eliminate cyber threats and potential outages early. The implementation of such a system can significantly improve the security and reliability of industrial processes that are key to various industries such as energy, manufacturing and transportation.