A Question of Context: Enhancing Intrusion Detection by Providing Context Information

Simon Duque Antón, Daniel Fraunholz, Stephan Teuber, Hans Dieter Schotten

In: 2017 Internet of Things Business Models, Users, and Networks. Conference of Telecommunication, Media and Internet Techno-Economics (CTTE-17) 13th befindet sich Joint CTTE and CMI November 23-24 Copenhagen Denmark IEEE 2017.


Due to the fourth industrial revolution, and the resulting increase in interconnectivity, industrial networks are more and more opened to publicly available networks. Apart from the huge benefit in manageability and flexibility, the openness also results in a larger attack surface for malicious adversaries. In comparison to office environments, industrial networks have very high volumes of data. In addition to that, every delay will most likely lead to loss of revenue. Hence, intrusion detection systems for industrial applications have different requirements than office-based intrusion detection systems. On the other hand, industrial networks are able to provide a lot of contextual information due to manufacturing execution systems and enterprise resource planning. Additionally, industrial networks tend to be more uniform, making it easier to determine outliers. In this work, an abstract simulation of industrial network behaviour is created. Malicious actions are introduced into a set of sequences of valid behaviour. Finally, a context-based and context-less intrusion detection system is used to find the attacks. The results are compared and commented. It can be seen that context information can help in identifying malicious actions more reliable than intrusion detection with only one source of information, e.g. the network.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence