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There are hardly any data sets publicly available that can be used toevaluate intrusion detection algorithms. The biggest threat for industrialapplications arises from state-sponsored and criminal groups. Often, formerlyunknown exploits are employed by these attackers, so-called 0-day exploits.They cannot be discovered with signature-based intrusion detection. Thus,statistical or machine learning based anomaly detection lends itself readily.These methods especially, however, need a large amount of labelled trainingdata. In this work, an exemplary industrial use case with real-world industrialhardware is presented. Siemens S7 Programmable Logic Controllers are used tocontrol a real world-based control application using the OPC UA protocol: Apump, filling and emptying water tanks. This scenario is used to generateapplication specific network data. Furthermore, attacks are introduced intothis data set. This is done in three ways: First, the normal process ismonitored and captured. Common attacks are then synthetically introduced intothis data set. Second, malicious behaviour is implemented on the ProgrammableLogic Controller program and executed live, the traffic is captured as well.Third, malicious behaviour is implemented on the Programmable Logic Controllerwhile still keeping the same output behaviour as in normal operation. Anattacker could exploit an application but forge valid sensor output so that noanomaly is detected. Sensors are employed, capturing temperature, sound andflow of water to create data that can be correlated to the network data andused to still detect the attack. All data is labelled, containing the groundtruth, meaning all attacks are known and no unknown attacks occur. This makesthem perfect for training of anomaly detection algorithms. The data ispublished to enable security researchers to evaluate intrusion detectionsolutions.

Implementing SCADA Scenarios and Introducing Attacks to Obtain Training Data for Intrusion Detection Methods