# Optimized Ensemble Model Towards Secured Industrial IoT Devices

arXiv: https://arxiv.org/abs/2401.05509 · HTML: https://mlsanity.com/paper/2401.05509/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [Tree-based Intelligent Intrusion Detection System in Internet of Vehicles](https://mlsanity.com/paper/1910.08635/) (2019)
- [Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection](https://mlsanity.com/paper/2008.03297/) (2020)
- [Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2101.03655/) (2021)
- [Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection](https://mlsanity.com/paper/2008.02327/) (2020)
- [Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach](https://mlsanity.com/paper/2012.11325/) (2020)
- [A Transfer Learning Framework for Anomaly Detection Using Model of Normality](https://mlsanity.com/paper/2011.06210/) (2020)
- [Ensemble-based Feature Selection and Classification Model for DNS Typo-squatting Detection](https://mlsanity.com/paper/2006.09272/) (2020)
- [SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach](https://mlsanity.com/paper/1904.00753/) (2019)
- [Optimized Random Forest Model for Botnet Detection Based on DNS Queries](https://mlsanity.com/paper/2012.11326/) (2020)
- [Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review](https://mlsanity.com/paper/2005.12267/) (2020)

## Abstract (arXiv)

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.