Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things

TL;DR

This paper explores machine learning techniques for detecting cyber-attacks on Industrial Internet of Things (IIoT) devices, including a real-world testbed demonstrating effective intrusion detection against various attack types.

Contribution

It presents a comprehensive analysis of IIoT vulnerabilities, reviews existing ML-based intrusion detection solutions, and provides a case study with a real-world testbed for evaluating ML-driven security methods.

Findings

ML-based anomaly detection effectively identifies cyber-attacks

Testbed results show high detection accuracy for various attack types

The approach demonstrates practical viability in real-world IIoT security

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 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.