A new framework for defect detection using hybird machine learning techniques
| dc.contributor.advisor | Ibrahim, Abdullahi Abdu | |
| dc.contributor.author | Mansour, Fatma Suleman Basher | |
| dc.date.accessioned | 2023-09-13T11:57:29Z | |
| dc.date.available | 2023-09-13T11:57:29Z | |
| dc.date.issued | 2022 | en_US |
| dc.date.submitted | 2022 | |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı | en_US |
| dc.description.abstract | In 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 Fmeasure. 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.citation | Mansour, F. S. B. (2022). A new framework for defect detection using hybird machine learning techniques. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/3937 | |
| dc.identifier.yoktezid | 737179 | |
| dc.institutionauthor | Mansour, Fatma Suleman Basher | |
| dc.language.iso | en | |
| dc.publisher | Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü | en_US |
| dc.relation.publicationcategory | Tez | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | SVM | en_US |
| dc.subject | Computer Security | en_US |
| dc.subject | Defect detection | en_US |
| dc.subject | ICA | en_US |
| dc.title | A new framework for defect detection using hybird machine learning techniques | |
| dc.type | Master Thesis |
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