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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Profiling Communications in Industrial IP Networks: Model Complexity and Anomaly Detection
verfasst von : Mustafa Amir Faisal, Alvaro A. Cardenas, Avishai Wool
Erschienen in: Security and Privacy Trends in the Industrial Internet of Things
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Abstract
Profiling communication patterns between devices in the Industrial Internet of Things (IIoT) ecosystems is important for deploying security measures like detecting anomalies and potential cyber-attacks. In this chapter we perform deep-packet inspection of various industrial protocols to generate models of communications between pairs of IIoT devices; in particular, we use discrete-time Markov chain models applied to four different industrial networks: (1) an electrical substation, (2) a small-scale water testbed, (3) a large-scale water treatment facility, and (4) an energy management system of a university campus. These datasets represent a variety of modern industrial protocols communicating over IP-compatible networks, including EtherNet/IP (Ethernet/Industrial Protocol), DNP3 (Distributed Network Protocol), and Modbus/TCP (Transmission Control Protocol).