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Interconnectivity of production machines is a key feature of the IndustrialInternet of Things (IIoT). This feature allows for many advantages inproducing. Configuration and maintenance gets easier, as access to the givenproduction unit is not necessarily coupled to physical presence. Customizedproduction of goods is easily possible, reducing production times andincreasing throughput. There are, however, also dangers to the increasingtalkativeness of industrial production machines. The more open a system is, themore points of entry for an attacker exist. Furthermore, the amount of data aproduction site also increases rapidly due to the integrated intelligence andinterconnectivity. To keep track of this data in order to detect attacks anderrors in the production site, it is necessary to smartly aggregate andevaluate the data. In this paper, we present a new approach for collecting,aggregating and analysing data from different sources and on three differentlevels of abstraction. Our model is event-centric, considering every occurrenceof information inside the system as an event. In the lowest level ofabstraction, singular packets are collected, correlated with log-entries andanalysed. On the highest level of abstraction, networks are pictured as aconnectivity graph, enriched with information about host-based activities.Furthermore, we describe our work in progress of evaluating our aggregationmodel on two different system settings. In the first scenario, we verify theusability of our model in a remote maintenance application. In the secondscenario, we evaluate our model in the context of network sniffing andcorrelation with log-files. First results show that our model is a promisingsolution to cope with increasing amounts of data and to correlate informationfrom different types of sources.

Highly Scalable and Flexible Model for Effective Aggregation of Context-based Data in Generic IIoT Scenarios