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

By Maede Zolanvari, M. Teixeira, Lav Gupta, Khaled Md. Khan, Raj Jain. IEEE Internet of Things Journal, 2019

386 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/274a0c1b9bbf1f7e3f4ba3fc63b1061c2f709b85

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In Artificial Intelligence

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In Control and Systems Engineering

Citations in the corpus

Cybersecurity for industrial control systems: A survey (2020, methodology): “In this section we discuss four specific case studies for multi-cloud deployment of ICSs and applicability of machine learning techniques for ICS security, which are: (1) Cyber risk assessments based on machine learning for the ICSs [82], (2) Cloud-based computing in these systems and how it improve”
Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning (2022, background): “IIoT has been successful in producing significant resource savings, while increasing productivity [2].”
A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities (2020, background): “For a more thorough overview of the state machine learning for industrial IDSs, we refer the interested reader to [207].”
TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems (2020, background): “To perform injection attacks two bash scripts (e.g., injection-1.sh and injection-2.sh) were written to inject data inputs against web applications (e.g., DVWA and the vulnerable public PHP) and Cross-Site Scripting (XSS) [60] often attempts to run malicious commands on a Web server in the IIoT appl”