Siemens Aktiengesellschaft

Josep Soler Garrido

Hans-Georg Köpken

Ingo Thon

Jan Kissling

Marco Caselli

A first mapping component produces observed triple statements from events received from a security tool monitoring an industrial system. A link-prediction component estimates a probability score for each observed triple statement by link prediction in a knowledge graph. A scoring component computes a priority score for an entity of the industrial system contained in the knowledge graph and/or events based on the probability scores. Priority scores can be computed for some or all possible events in the industrial system as a reference to prioritize alerts coming from the security tools. The system works in an unsupervised manner. In fact, the system does not directly try to infer (predict) maliciousness in entities or events on the knowledge graph. Instead, priority scores are used during operation to evaluate actual system observations and prioritize them. Therefore, a knowledge graph-based recommendation system for automated analysis of industrial cybersecurity events is provided.

Method and system for automated analysis of industrial cybersecurity events

Provided is a system and method for minimizing non-productive idle times within an automation process executed by an automation facility, the system including a model memory which stores a probabilistic model of a distribution of object exchange times of objects used or consumed in the automation process; and an optimizer adapted to calculate optimal assignments of objects to magazine positions of an object storage magazine depending on the probabilistic model and depending on a sequence of productive non-idle times of objects used or consumed in process steps of the automation process.

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Seville, Spain

Josep Soler Garrido