| Full text | |
| Author(s): |
Zolanvari, Maede
[1]
;
Teixeira, Marcio A.
[2]
;
Gupta, Lav
[1]
;
Khan, Khaled M.
[3]
;
Jain, Raj
[1]
Total Authors: 5
|
| Affiliation: | [1] Washington Univ, Dept Comp Sci & Engn, St Louis, MO 63130 - USA
[2] Fed Inst Educ Sci Technol Sao Paulo, BR-01109010 Sao Paulo - Brazil
[3] Qatar Univ, Dept Comp Sci & Engn, Doha - Qatar
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | IEEE INTERNET OF THINGS JOURNAL; v. 6, n. 4, p. 6822-6834, AUG 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 17/01055-4 - Management platform for deployment and distribution of applications in multi-cloud environmen |
| Grantee: | Marcio Andrey Teixeira |
| Support Opportunities: | Scholarships abroad - Research |