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Smart city article details

Title	Intrusion Detection Using Chaotic Poor And Rich Optimization With Deep Learning Model For Smart City Environment
ID_Doc	33347
Authors	Alrayes F.S.; Asiri M.M.; Maashi M.; Salama A.S.; Hamza M.A.; Ibrahim S.S.; Zamani A.S.; Alsaid M.I.
Year	2023
Published	Sustainability (Switzerland), 15, 8
DOI	http://dx.doi.org/10.3390/su15086902
Abstract	Artificial intelligence (AI) techniques play a vital role in the evolving growth and rapid development of smart cities. To develop a smart environment, enhancements to the execution, sustainability, and security of traditional mechanisms become mandatory. Intrusion detection systems (IDS) can be considered an effective solutions to achieve security in the smart environment. This article introduces intrusion detection using chaotic poor and rich optimization with a deep learning model (IDCPRO-DLM) for ubiquitous and smart atmospheres. The IDCPRO-DLM model follows preprocessing, feature selection, and classification stages. At the initial stage, the Z-score data normalization system is exploited to scale the input data. Additionally, the IDCPRO-DLM method designs a chaotic poor and rich optimization algorithm-based feature selection (CPROA-FS) approach for selecting feature subsets. For intrusion detection, butterfly optimization algorithm (BOA) with a deep sparse autoencoder (DSAE) is used. The simulation analysis of the IDCPRO-DLM technique is studied on the benchmark CICIDS dataset and the comparison results show the better performance of the IDCPRO-DLM algorithm over recent state-of-the-art approaches with a maximum accuracy of 98.53%. © 2023 by the authors.
Author Keywords	deep learning; feature selection; intrusion detection system; security; smart cities; smart environment

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