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AuthorZolanvari M.
AuthorTeixeira M.A.
AuthorGupta L.
AuthorKhan K.M.
AuthorJain R.
Available date2020-04-27T08:34:19Z
Publication Date2019
Publication NameIEEE Internet of Things Journal
ResourceScopus
ISSN23274662
URIhttp://dx.doi.org/10.1109/JIOT.2019.2912022
URIhttp://hdl.handle.net/10576/14545
AbstractIt 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. - 2014 IEEE.
SponsorManuscript received January 16, 2019; revised April 1, 2019 and April 12, 2019; accepted April 13, 2019. Date of publication April 18, 2019; date of current version July 31, 2019. This work was supported by NPRP through the Qatar National Research Fund (a member of Qatar Foundation) under Grant NPRP 10-901-2-370. The work of M. A. Teixeira was supported in part by the São Paulo Research Foundation (FAPESP) under Grant 2017/01055-4 and in part by the Instituto Federal de Educação, Ciência e Tecnologia de São Paulo. (Corresponding author: Maede Zolanvari.)
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCyber attack
Industrial Internet of Things (IIoT)
intrusion detection
machine learning (ML)
network security
supervisory control and data acquisition (SCADA)
vulnerability assessment
TitleMachine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things
TypeArticle
Pagination6822-6834
Issue Number4
Volume Number6
dc.accessType Open Access


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