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Erschienen in:
Securing Industrial Control Systems Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Who’s There? Evaluating Data Source Integrity and Veracity in IIoT Using Multivariate Statistical Process Control

verfasst von : Iñaki Garitano, Mikel Iturbe, Enaitz Ezpeleta, Urko Zurutuza

Erschienen in: Security and Privacy Trends in the Industrial Internet of Things

Verlag: Springer International Publishing

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Abstract

The security landscape in Industrial settings has completely changed in the last decades. From the initial primitive setups, industrial networks have evolved into massively interconnected environments, thus developing the Industrial Internet of Things (IIoT) paradigm. In IIoT, multiple, heterogeneous devices collaborate by collecting, sending and processing data. These data-driven environments have made possible to develop added-value services based on data that improve industrial process operation. However, it is necessary to audit incoming data to determine that the decisions are made based on correct data. In this chapter, we present an IIoT Anomaly Detection System (ADS), that audits the integrity and veracity of the data received from incoming connections. For this end, the ADS includes field data (physical qualities based on data) and connection metadata (interval between incoming connections and packet size) in the same anomaly detection model. The approach is based on multivariate statistical process Control and has been validated using data from a real water distribution plant.

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Vorheriges Kapitel Improving Security in Industrial Internet of Things: A Distributed Intrusion Detection Methodology
Nächstes Kapitel Secure Machine to Machine Communication in Industrial Internet of Things
Fußnoten
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Metadaten
Titel
Who’s There? Evaluating Data Source Integrity and Veracity in IIoT Using Multivariate Statistical Process Control
verfasst von
Iñaki Garitano
Mikel Iturbe
Enaitz Ezpeleta
Urko Zurutuza
Copyright-Jahr
2019
Buch
Security and Privacy Trends in the Industrial Internet of Things

Print ISBN: 978-3-030-12329-1

Electronic ISBN: 978-3-030-12330-7

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-12330-7_9