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Deep Learning Techniques for Web-Based Attack Detection in Industry 5.0: A Novel Approach
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1 of the 45 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-11): 10.1007/s00500-022-06755-z (notice 2026-02-02).
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A Survey on the Role of Artificial Intelligence, Machine Learning and Deep Learning for Cybersecurity Attack Detection
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Signature-based and Behavior-based Attack Detection with Machine Learning for Home IoT Devices
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inferredcites →https://doi.org/10.32604/iasc.2023.038043openalex-snapshot
Mirai Botnet Attack Detection in Low-Scale Network Traffic
inferredcites →https://doi.org/10.3390/machines10100858openalex-snapshot
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inferredcites →https://doi.org/10.3390/su11164371openalex-snapshot
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Enhanced Security and Privacy from Industry 4.0 and 5.0 Vision
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inferred← citeshttps://doi.org/10.1007/978-981-96-3253-4_4openalex-snapshot
Deep Learning-Driven Attack Detection in IoT Networks: A Comprehensive Survey
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Exploring the Effect of Sequence Smoothness on Machine Learning Accuracy
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inferred← citeshttps://doi.org/10.1007/s10791-025-09678-zopenalex-snapshot
A novel integration of multi-stocked gated variant recurrent units and Kolmogorov-Arnold tuned deep training networks for anchoring the intrusion detection against computer attacks
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inferred← citeshttps://doi.org/10.1007/s12597-026-01168-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.engappai.2025.113404openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.eswa.2025.126982openalex-snapshot
LLM-AE-MP: Web Attack Detection Using a Large Language Model with Autoencoder and Multilayer Perceptron
inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103347openalex-snapshot
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Comparison of Multiple Feature Selection Techniques for Machine Learning-Based Detection of IoT Attacks
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