A new framework for defect detection using hybird machine learning techniques
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Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
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 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.
Açıklama
Anahtar Kelimeler
Computer Security, Defect Detection, ICA, SVM
Kaynak
ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Mansour, 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.
