Generating Network Intrusion Image through IGTD Algorithm for CNN Classification

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dc.contributor.authorNazarri, M.N.A.A.
dc.contributor.authorYusof, M.H.M.
dc.contributor.authorAlmohammedi, A.A.
dc.date.accessioned2024-09-29T16:20:54Z
dc.date.available2024-09-29T16:20:54Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description3rd International Conference on Computing and Information Technology, ICCIT 2023 -- 13 September 2023 through 14 September 2023 -- Tabuk -- 193403en_US
dc.description.abstractStealthy attacks make Intrusion Detection System (IDS) research perennial and notably important. From the literature, a few problems were identified in IDS studies particularly with regards to image generation. Image generation is the preprocessing phase during the development of CNN classifier. Simple feature extraction process that are applied in a few studies may lose features' details. They disregard spatial information such as pair pixels information. Secondly several public datasets suffer from realistic network traits. This study will apply the improvised version of IGTD algorithm to resolve image generation process. IGTD algorithm has been adopted in Cancer Cell Lines (CCLs) study. Realistic dataset of CIRA-CIC-DoHBrw-2020 and CICIDS2017 will be run over the algorithm. These dataset have been reviewed to be the closest realistic production network dataset. Result shows the generated image has notable difference between attack and benign traffic. The highest trained classification model has achieved almost 80% of accuracy rate. © 2023 IEEE.en_US
dc.description.sponsorshipUiTM), Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA (UiTM) Tapah campus and Machine Learning, (FRGS/1/2021/ICT07/UITM/02/3); Universiti Teknologi MARA, UiTM; Research Management Centre, International Islamic University Malaysia, RMCen_US
dc.identifier.doi10.1109/ICCIT58132.2023.10273902
dc.identifier.endpage177en_US
dc.identifier.isbn979-835032148-7
dc.identifier.scopus2-s2.0-85175449782en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage172en_US
dc.identifier.urihttps://doi.org/10.1109/ICCIT58132.2023.10273902
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9407
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 3rd International Conference on Computing and Information Technology, ICCIT 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCNN classificationen_US
dc.subjectDeep Learning IDSen_US
dc.subjectIDSen_US
dc.subjectImage Generator Tabular Dataseten_US
dc.subjectIntrusion Detection Systemen_US
dc.subjectMachine Learning in Cybersecurityen_US
dc.titleGenerating Network Intrusion Image through IGTD Algorithm for CNN Classificationen_US
dc.typeConference Objecten_US

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