Optimized Ensemble Model Towards Secured Industrial IoT Devices

TL;DR

This paper introduces an optimized ensemble machine learning framework using Bayesian Optimization-Gaussian Process to enhance attack detection accuracy in industrial IoT devices, addressing security challenges in increasingly connected manufacturing environments.

Contribution

It presents a novel ensemble learning framework combining BO-GP with tree-based models for improved intrusion detection in IIoT, validated on real-world data.

Findings

Enhanced detection accuracy over standard models

Improved precision and F-score in experiments

Effective in identifying network attacks in IIoT environments

Abstract

The continued growth in the deployment of Internet-of-Things (IoT) devices has been fueled by the increased connectivity demand, particularly in industrial environments. However, this has led to an increase in the number of network related attacks due to the increased number of potential attack surfaces. Industrial IoT (IIoT) devices are prone to various network related attacks that can have severe consequences on the manufacturing process as well as on the safety of the workers in the manufacturing plant. One promising solution that has emerged in recent years for attack detection is Machine learning (ML). More specifically, ensemble learning models have shown great promise in improving the performance of the underlying ML models. Accordingly, this paper proposes a framework based on the combined use of Bayesian Optimization-Gaussian Process (BO-GP) with an ensemble tree-based learning model to improve the performance of intrusion and attack detection in IIoT environments. The proposed framework's performance is evaluated using the Windows 10 dataset collected by the Cyber Range and IoT labs at University of New South Wales. Experimental results illustrate the improvement in detection accuracy, precision, and F-score when compared to standard tree and ensemble tree models.