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The digitisation of industry provides a plethora of novel applications thatincrease flexibility and reduce setup and maintenance time as well as cost.Furthermore, novel use cases are created by the digitisation of industry,commonly known as Industry 4.0 or the Industrial Internet of Things,applications make use of communication and computation technology that isbecoming available. This enables novel business use cases, such as the digitaltwin, customer individual production, and data market places. However, theinter-connectivity such use cases rely on also significantly increases theattack surface of industrial enterprises. Sabotage and espionage are aimed atdata, which is becoming the most crucial asset of an enterprise. Since therequirements on security solutions in industrial networks are inherentlydifferent from office networks, novel approaches for intrusion detection needto be developed. In this work, process data of a real water treatment processthat contains attacks is analysed. Analysis is performed by an extension ofMatrix Profiles, a motif discovery algorithm for time series. By extendingMatrix Profiles with a Hammingdistance metric, binary and tertiary actuatorscan be integrated into the analysis in a meaningful fashion. This algorithmrequires low training effort while providing accurate results. Furthermore, itcan be employed in a real-time fashion. Selected actuators in the data set areanalysed to highlight the applicability of the extended Matrix Profiles.

Intrusion Detection in Binary Process Data: Introducing the Hamming-distance to Matrix Profiles