A Stacked Ensemble Classifier for an Intrusion Detection System in the Edge of IoT and IIoT Networks

Oliveira, Giovanni Aparecido Da Silva; Lima, Priscila Serra Silva; Kon, Fábio; Terada, Routo; Batista, Daniel Macêdo; Hirata, Roberto; Hamdan, Mosab

doi:10.1109/latincom56090.2022.10000559

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…The authors propose using Artificial Intelligence techniques, particularly Deep Learning models, to enhance the performance of an IDS and reduce cyberattacks on IoT and IIoT networks. To achieve this, the authors employ an ensemble classifier method that combines multiple prediction algorithms to create a new model with greater precision in [13]. The authors incorporate an Artificial Neural Network, which updates all model weights based on training data to achieve optimal performance.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing

Sezgin¹,

Boyacı²

2023

Adv. Sci. Technol. Res. J.

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Industrial Internet of Things (IIoT) is a rapidly growing field, where interconnected devices and systems are used to improve operational efficiency and productivity. However, the extensive connectivity and data exchange in the IIoT environment make it vulnerable to cyberattacks. Intrusion detection systems (IDS) are used to monitor IIoT networks and identify potential security breaches. Feature selection is an essential step in the IDS process, as it can reduce computational complexity and improve the accuracy of the system. In this research paper, we propose a hybrid feature selection approach for intrusion detection in the IIoT environment using Shapley values and a genetic algorithm-based automated preprocessing technique which has three automated steps including imputation, scaling and feature selection. Shapley values are used to evaluate the importance of features, while the genetic algorithm-based automated preprocessing technique optimizes feature selection. We evaluate the proposed approach on a publicly available dataset and compare its performance with existing state-of-the-art methods. The experimental results demonstrate that the proposed approach outperforms existing methods, achieving high accuracy, precision, recall, and F1-score. The proposed approach has the potential to enhance the performance of IDS in the IIoT environment and improve the overall security of critical industrial systems.

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Section: Related Workmentioning

confidence: 99%

Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing

Sezgin¹,

Boyacı²

2023

Adv. Sci. Technol. Res. J.

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“…The proposal from Oliveira et al [175] addresses the challenge of finding the correct method for Ensemble classification, particularly in the context of Machine Learning in IoT. To tackle this issue, they present a Deep Learning-based classification approach.…”

Section: H Work Published In 2022mentioning

confidence: 99%

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Lucas,

de Figueiredo,

Tojeiro

et al. 2023

IEEE Access

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Machine learning algorithms present a robust alternative for building Intrusion Detection Systems due to their ability to recognize attacks in computer network traffic by recognizing patterns in large amounts of data. Typically, classifiers are trained for this task. Together, ensemble learning algorithms have increased the performance of these detectors, reducing classification errors and allowing computer networks to be more protected. This research presents a comprehensive Systematic Review of the Literature where works related to intrusion detection with ensemble learning were obtained from the most relevant scientific bases. We offer 188 works, several compilations of datasets, classifiers, and ensemble algorithms, and document the experiments that stood out in their performance. A characteristic of this research is its originality. We found two surveys in the literature specifically focusing on the relationship between ensemble techniques and intrusion detection. We present for the last eight years covered by this survey a timeline-based view of the works studied to highlight evolutions and trends. The results obtained by our survey show a growing area, with excellent results in detecting attacks but with needs for improvement in pruning for choosing classifiers, which makes this work unprecedented for this context.

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Improving ML/DL Solutions for Anomaly Detection in IoT Environments

Tamani,

El-Jaouhari,

Khan

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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A Stacked Ensemble Classifier for an Intrusion Detection System in the Edge of IoT and IIoT Networks

Cited by 5 publications

References 25 publications

Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing

Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Improving ML/DL Solutions for Anomaly Detection in IoT Environments

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