Optimized deep autoencoder model for internet of things intruder detection
The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the reduction of communication energy, lowering latency, and protecting data privacy. Motivated by these benefits, this research...
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Institute of Electrical and Electronics Engineers Inc.
2022
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Online Access: | http://eprints.utm.my/104365/1/HusseinSalemAli2022_OptimizedDeepAutoencoderModelforInternet.pdf http://eprints.utm.my/104365/ http://dx.doi.org/10.1109/ACCESS.2022.3144208 |
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my.utm.1043652024-02-04T09:36:22Z http://eprints.utm.my/104365/ Optimized deep autoencoder model for internet of things intruder detection Lahasan, Badr Samma, Hussein QA75 Electronic computers. Computer science The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the reduction of communication energy, lowering latency, and protecting data privacy. Motivated by these benefits, this research aims to design a lightweight autoencoder deep model that has a shallow architecture with a small number of input features and a few hidden neurons. To achieve this objective, an efficient two-layer optimizer is used to evolve a lightweight deep autoencoder model by performing simultaneous selection for the input features, the training instances, and the number of hidden neurons. The optimized deep model is constructed guided by both the accuracy of a K-nearest neighbor (KNN) classifier and the complexity of the autoencoder model. To evaluate the performance of the proposed optimized model, it has been applied for the N-baiot intrusion detection dataset. Reported results showed that the proposed model achieved anomaly detection accuracy of 99%with a lightweight autoencoder model with on average input features around 30 and output hidden neurons of 2 only. In addition, the proposed two-layers optimizer was able to outperform several optimizers such as Arithmetic Optimization Algorithm (AOA), Particle Swarm Optimization (PSO), and Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO). Institute of Electrical and Electronics Engineers Inc. 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/104365/1/HusseinSalemAli2022_OptimizedDeepAutoencoderModelforInternet.pdf Lahasan, Badr and Samma, Hussein (2022) Optimized deep autoencoder model for internet of things intruder detection. IEEE Access, 10 (NA). pp. 8434-8448. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2022.3144208 DOI : 10.1109/ACCESS.2022.3144208 |
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QA75 Electronic computers. Computer science Lahasan, Badr Samma, Hussein Optimized deep autoencoder model for internet of things intruder detection |
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The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the reduction of communication energy, lowering latency, and protecting data privacy. Motivated by these benefits, this research aims to design a lightweight autoencoder deep model that has a shallow architecture with a small number of input features and a few hidden neurons. To achieve this objective, an efficient two-layer optimizer is used to evolve a lightweight deep autoencoder model by performing simultaneous selection for the input features, the training instances, and the number of hidden neurons. The optimized deep model is constructed guided by both the accuracy of a K-nearest neighbor (KNN) classifier and the complexity of the autoencoder model. To evaluate the performance of the proposed optimized model, it has been applied for the N-baiot intrusion detection dataset. Reported results showed that the proposed model achieved anomaly detection accuracy of 99%with a lightweight autoencoder model with on average input features around 30 and output hidden neurons of 2 only. In addition, the proposed two-layers optimizer was able to outperform several optimizers such as Arithmetic Optimization Algorithm (AOA), Particle Swarm Optimization (PSO), and Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO). |
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Article |
author |
Lahasan, Badr Samma, Hussein |
author_facet |
Lahasan, Badr Samma, Hussein |
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Lahasan, Badr |
title |
Optimized deep autoencoder model for internet of things intruder detection |
title_short |
Optimized deep autoencoder model for internet of things intruder detection |
title_full |
Optimized deep autoencoder model for internet of things intruder detection |
title_fullStr |
Optimized deep autoencoder model for internet of things intruder detection |
title_full_unstemmed |
Optimized deep autoencoder model for internet of things intruder detection |
title_sort |
optimized deep autoencoder model for internet of things intruder detection |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2022 |
url |
http://eprints.utm.my/104365/1/HusseinSalemAli2022_OptimizedDeepAutoencoderModelforInternet.pdf http://eprints.utm.my/104365/ http://dx.doi.org/10.1109/ACCESS.2022.3144208 |
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13.159267 |