Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning

TL;DR

This paper investigates how imbalanced datasets affect machine learning-based security in industrial IoT environments, highlighting challenges and demonstrating findings through real-world testbed experiments.

Contribution

It provides a detailed analysis of the impact of dataset imbalance on ML security methods in IIoT and offers experimental insights using a realistic industrial testbed.

Findings

Imbalanced datasets degrade ML security performance in IIoT.

Real-world testbed experiments reveal specific challenges.

Highlights need for balanced data in IIoT security models.

Abstract

Machine learning algorithms have been shown to be suitable for securing platforms for IT systems. However, due to the fundamental differences between the industrial internet of things (IIoT) and regular IT networks, a special performance review needs to be considered. The vulnerabilities and security requirements of IIoT systems demand different considerations. In this paper, we study the reasons why machine learning must be integrated into the security mechanisms of the IIoT, and where it currently falls short in having a satisfactory performance. The challenges and real-world considerations associated with this matter are studied in our experimental design. We use an IIoT testbed resembling a real industrial plant to show our proof of concept.