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).
| Original language | English |
|---|---|
| Title of host publication | Advanced Sciences and Technologies for Security Applications |
| Publisher | Springer |
| Pages | 139-160 |
| Number of pages | 22 |
| DOIs | |
| State | Published - 2019 |
Publication series
| Name | Advanced Sciences and Technologies for Security Applications |
|---|
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 6 Clean Water and Sanitation -
SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Anomaly detection
- DTMC
- IIoT
- Modeling
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Safety Research
- Political Science and International Relations
- Computer Science Applications
- Computer Networks and Communications
- Health, Toxicology and Mutagenesis
Fingerprint
Dive into the research topics of 'Profiling communications in industrial ip networks: Model complexity and anomaly detection'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver