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dc.contributor.authorDiaba, Sayawu Yakubu
dc.contributor.authorAnafo, Theophilus
dc.contributor.authorTetteh, Lord Anertei
dc.contributor.authorOyibo, Michael Alewo
dc.contributor.authorAlola, Andrew Adewale
dc.contributor.authorShafie-khah, Miadreza
dc.contributor.authorElmusrati, Mohammed
dc.identifier.citationNeural Networks. 2023, 165 321-332.en_US
dc.description.abstractSupervisory Control and Data Acquisition (SCADA) systems are computer-based control architectures specifically engineered for the operation of industrial machinery via hardware and software models.These systems are used to project, monitor, and automate the state of the operational network through the utilization of ethernet links, which enable two-way communications. However, as a result of their constant connectivity to the internet and the lack of security frameworks within their internal architecture, they are susceptible to cyber-attacks. In light of this, we have proposed an intrusion detection algorithm, intending to alleviate this security bottleneck. The proposed algorithm, the Genetically Seeded Flora (GSF) feature optimization algorithm, is integrated with Transformer Neural Network (TNN) and functions by detecting changes in operational patterns that may be indicative of an intruder’s involvement. The proposed Genetically Seeded Flora Transformer Neural Network (GSFTNN) algorithm stands in stark contrast to the signature-based method employed by traditional intrusion detection systems. To evaluate the performance of the proposed algorithm, extensive experiments are conducted using the WUSTL-IIOT-2018 ICS SCADA cyber security dataset. The results of these experiments indicate that the proposed algorithm outperforms traditional algorithms such as Residual Neural Networks (ResNet), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) in terms of accuracy and efficiency.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectgenetically seeded floraen_US
dc.subjectintrusion detection systemsen_US
dc.subjectlong short-term memoryen_US
dc.subjectrecurrent neural networken_US
dc.subjectresidual neural network and transformer neural networken_US
dc.titleSCADA securing system using deep learning to prevent cyber infiltrationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2023 The Author(s). Published by Elsevier Ltd.en_US
dc.source.journalNeural Networksen_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal