A Two-Layer Soft-Voting Ensemble Learning Model For Network Intrusion Detection

Yao, Wenbin; Hu, Longcan; Hou, Yanzhao; Li, Xiaoyong

doi:10.1109/dsn-w54100.2022.00034

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…Ensemble Learning [27][28][29][30][31][32][33] solves problems by training multiple learners and combining them. Ensemble is better at generalization than weak learners, and can turn weak learners that are only slightly better than random guess into strong learners with accurate prediction.…”

Section: Related Workmentioning

confidence: 99%

“…Saba [32] proposed a two-stage hybrid method, selected appropriate features using the genetic algorithm, employed an ensemble classifier, and applied SVM and decision tree to mark the attack as malicious or normal. Yao [33] proposed a two-layer soft-voting ensemble learning model with RF, lightGBM and XGBoost as base classifiers, and used the adversarial validate algorithm to test the consistency of the data distribution in training and testing dataset to determine whether the dataset needs re-splitting. The results showed that the model has a higher accuracy rate in both binary and multi-classification than other One-Class Classification models.…”

Section: Related Workmentioning

confidence: 99%

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A Lightweight Intelligent Network Intrusion Detection System Using One-Class Autoencoder and Ensemble Learning for IoT

Yao

Hou

et al. 2023

Sensors

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Network intrusion detection technology is key to cybersecurity regarding the Internet of Things (IoT). The traditional intrusion detection system targeting Binary or Multi-Classification can detect known attacks, but it is difficult to resist unknown attacks (such as zero-day attacks). Unknown attacks require security experts to confirm and retrain the model, but new models do not keep up to date. This paper proposes a Lightweight Intelligent NIDS using a One-Class Bidirectional GRU Autoencoder and Ensemble Learning. It can not only accurately identify normal and abnormal data, but also identify unknown attacks as the type most similar to known attacks. First, a One-Class Classification model based on a Bidirectional GRU Autoencoder is introduced. This model is trained with normal data, and has high prediction accuracy in the case of abnormal data and unknown attack data. Second, a multi-classification recognition method based on ensemble learning is proposed. It uses Soft Voting to evaluate the results of various base classifiers, and identify unknown attacks (novelty data) as the type most similar to known attacks, so that exception classification becomes more accurate. Experiments are conducted on WSN-DS, UNSW-NB15, and KDD CUP99 datasets, and the recognition rates of the proposed models in the three datasets are raised to 97.91%, 98.92%, and 98.23% respectively. The results verify the feasibility, efficiency, and portability of the algorithm proposed in the paper.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Lightweight Intelligent Network Intrusion Detection System Using One-Class Autoencoder and Ensemble Learning for IoT

Yao

Hou

et al. 2023

Sensors

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“…The work by Yao et al [165] addresses the challenges of data imbalance and low accuracy. To tackle this, the authors employ an ensemble with the classifiers XGBoost, LightGBM, and Random Forest on the UNSW-NB15 dataset.…”

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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