Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE internet of things journal Jg. 6; H. 4; S. 6822 - 6834
Hauptverfasser: Zolanvari, Maede, Teixeira, Marcio A., Gupta, Lav, Khan, Khaled M., Jain, Raj
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Analytics
Anomalies
Artificial intelligence
Cyber attack
Cybersecurity
Electromagnetic interference
Industrial applications
Industrial Internet of Things
Industrial Internet of Things (IIoT)
Internet of Things
intrusion detection
Intrusion detection systems
Literature reviews
Machine learning
machine learning (ML)
network security
Protocol (computers)
Query languages
Structured Query Language-SQL
supervisory control and data acquisition (SCADA)
Vulnerability
vulnerability assessment
ISSN:2327-4662, 2327-4662
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2019.2912022