Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things
This work addresses security vulnerabilities in IIoT systems, which is critical due to potentially devastating consequences of attacks, but it appears incremental as it applies existing machine learning methods to a specific domain.
The paper tackles the problem of securing Industrial Internet of Things (IIoT) devices by conducting a cyber-vulnerability assessment and designing an intrusion detection system using machine learning, demonstrating that a machine learning-based anomaly detection system performs well in detecting attacks such as backdoor, command injection, and SQL injection on a real-world testbed.
It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning 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 machine learning in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using machine learning 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 machine learning 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.