Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things

Maede Zolanvari, Marcio A. Teixeira, Lav Gupta, Khaled M. Khan, Raj Jain
2019 IEEE Internet of Things Journal  
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
more » ... lity 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. Index Terms-Cyber attack, Industrial Internet of Things (IIoT), intrusion detection, machine learning (ML), network security, supervisory control and data acquisition (SCADA), vulnerability assessment.
doi:10.1109/jiot.2019.2912022 fatcat:3j355nmcgrc6vpebupwq5bal4q