The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things
Open accessAbstract
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.
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Citations
Cites
Guide to Industrial Control Systems (ICS) Security
Stouffer, K., Pillitteri, V., Lightman, S., Abrams, M., & Hahn, A. (2015) · doi:10.6028/nist.sp.800-82r2 · Cited by 1,377 · cited 2×
“Our extensive study of relevant works (such as [9], [11], [12], [13], and [14]) reveals that these are the most common threats in the SCADA systems.”
BackgroundFisher, A., Rudin, C., & Dominici, F. (2018) · arXiv · doi:10.48550/arxiv.1801.01489 · Cited by 1,165
A Taxonomy of Cyber Attacks on SCADA Systems
Zhu, B., Joseph, A., & Sastry, S. (2011) · 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing · doi:10.1109/ithings/cpscom.2011.34 · Cited by 458 · cited 2×
“, water level) could be manipulated through this attack [10].”
BackgroundIIoT cybersecurity risk modeling for SCADA systems
Falco, G., Caldera, C., & Shrobe, H. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2822842 · Cited by 137
Cited by
Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., & Anwar, A. (2020) · IEEE Access · doi:10.1109/access.2020.3022862 · Cited by 892
Tange, K., De Donno, M., Fafoutis, X., & Dragoni, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011208 · Cited by 410
“For a more thorough overview of the state machine learning for industrial IDSs, we refer the interested reader to [207].”
BackgroundCybersecurity for industrial control systems: A survey
Bhamare, D., Zolanvari, M., Erbad, A., Jain, R., Khan, K., & Meskin, N. (2020) · Computers & Security · doi:10.1016/j.cose.2019.101677 · Cited by 394
Self-citationA machine learning-based intrusion detection for detecting internet of things network attacks
Kayode Saheed, Y., Idris Abiodun, A., Misra, S., Kristiansen Holone, M., & Colomo-Palacios, R. (2022) · Alexandria Engineering Journal · doi:10.1016/j.aej.2022.02.063 · Cited by 371
MQTTset, a New Dataset for Machine Learning Techniques on MQTT
Vaccari, I., Chiola, G., Aiello, M., Mongelli, M., & Cambiaso, E. (2020) · Sensors · doi:10.3390/s20226578 · Cited by 292
Metadata
- Authors
- Zolanvari, M., Teixeira, M. A., Gupta, L., Khan, K. M., & Jain, R.
- Venue
- IEEE Internet of Things Journal
- Published
- 2019-08
- Volume
- 6
- Issue
- 4
- Pages
- 6822-6834
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2940895343
- Cited by
- 525
- References
- 4
- FWCI
- 27.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Zolanvari, M., Teixeira, M. A., Gupta, L., Khan, K. M., & Jain, R. (2019). Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2019.2912022
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