A new framework for defect detection using hybird machine learning techniques
[ X ]
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
SVM, Computer Security, Defect detection, ICA
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
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.
