A new framework for defect detection using hybird machine learning techniques

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dc.contributor.authorMansour, Fatma Suleman
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.date.accessioned2022-12-27T16:46:52Z
dc.date.available2022-12-27T16:46:52Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractIn this study, some logs obtained with the Firewall Device are classified using multiclass support vector machine (SVM) classifier optimized by grid search algorithm. The presented method was compared with various data mining techniques. In addition, these learning algorithms were compared using four measures: Accuracy, Precision, Recall, and F-measure. In this paper, we propose the use of an automatic ICA-SVM to solve the defect problem in the computer network. It is the first automatic ICA to be used to reduce the size of input data. Then, the output of the ICA is connected to classifiers. SVM categorizes the attributes into three attacks (normal and abnormal). The proposed system showed results with an accuracy of 99.21% compared to some studies.en_US
dc.identifier.citationMansour, F. S., Ibrahim, A. A. (2022). A new framework for defect detection using hybird machine learning techniques. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 66-68). IEEE.en_US
dc.identifier.endpage68en_US
dc.identifier.isbn9781665470131
dc.identifier.scopus2-s2.0-85142806496
dc.identifier.scopusqualityN/A
dc.identifier.startpage66en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3156
dc.indekslendigikaynakScopus
dc.institutionauthorMansour, Fatma Suleman
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings
dc.relation.isversionof10.1109/ISMSIT56059.2022.9932856en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputer Securityen_US
dc.subjectDefect Detectionen_US
dc.subjectICAen_US
dc.subjectSVMen_US
dc.titleA new framework for defect detection using hybird machine learning techniques
dc.typeConference Object

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