Ensemble based Dimensionality Reduction for Intrusion Detection using Random Forest in Wireless Networks

Suhana, S.; Karthic, S.; Yuvaraj, N.

doi:10.1109/icssit55814.2023.10060929

Cited by 1 publication

(1 citation statement)

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“…Dimensionality reduction techniques and feature selection are also critical preprocessing steps for deriving optimal and minimal subsets of relevant input features, which facilitates more efficient learning [ 45 – 48 ]. Stacked autoencoders (SAEs) are widely used in unsupervised feature learning and dimensionality reduction to improve intrusion detection, and Muhammad et al [ 49 ] proposed the use of SAE with two latent layers, succeeded by a supervised deep neural network classifier.…”

Section: Related Workmentioning

confidence: 99%

Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection

Tawfik

2024

PLoS ONE

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The proliferation of Internet of Things (IoT) devices and fog computing architectures has introduced major security and cyber threats. Intrusion detection systems have become effective in monitoring network traffic and activities to identify anomalies that are indicative of attacks. However, constraints such as limited computing resources at fog nodes render conventional intrusion detection techniques impractical. This paper proposes a novel framework that integrates stacked autoencoders, CatBoost, and an optimised transformer-CNN-LSTM ensemble tailored for intrusion detection in fog and IoT networks. Autoencoders extract robust features from high-dimensional traffic data while reducing the dimensionality of the efficiency at fog nodes. CatBoost refines features through predictive selection. The ensemble model combines self-attention, convolutions, and recurrence for comprehensive traffic analysis in the cloud. Evaluations of the NSL-KDD, UNSW-NB15, and AWID benchmarks demonstrate an accuracy of over 99% in detecting threats across traditional, hybrid enterprises and wireless environments. Integrated edge preprocessing and cloud-based ensemble learning pipelines enable efficient and accurate anomaly detection. The results highlight the viability of securing real-world fog and the IoT infrastructure against continuously evolving cyber-attacks.

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Section: Related Workmentioning

confidence: 99%

Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection

Tawfik

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

Ensemble based Dimensionality Reduction for Intrusion Detection using Random Forest in Wireless Networks

Cited by 1 publication

References 16 publications

Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection

Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection

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