… We evaluated feature importance and feature sensitivity to measure the relevancy and … of
the selected features. We applied log transformation and feature scaling on the feature set and …

… scaled data feature results using one-class classification (OCSVM) model proposed in [8,
10, 18]. The model used to evaluate feature scaling … types of feature scaling methods used to …

… feature scaling, feature reduction and over sampling technique and determine the best
combination of techniques for intrusion detection … each categorical feature into new features and …

… intrusion detection system, reduction methods and classification techniques of machine
learning for intelligent intrusion detection … Step 2: feature scaling Due to large values in features …

… In this paper, a support vector machine (SVM) method with new nonlinear scaling is
proposed to detect intrusion, which is different from the linear scaling as a stage of feature …

… NON-LINEAR SCALING SCHEME … as classifier for intrusion detection. In this paper, we
propose a new SVM based intrusion detection technique with the non-linear scaling for the …

… using feature selection and feature scaling. This paper introduces a new feature scaling
technique … topics in anomaly-based intrusion detection: collecting data, feature selection, feature …

… In this research, we have adopted the Min-max approach for feature scaling and PCA for
FS. However, there are other feature scaling methods such as the z-score technique, and FS …

… An ML pipeline, which consists of sequential components for categorical feature
encoding and feature scaling together with the trained DNN, was developed to perform real-time …

… Training a classifier is a time-consuming process, especially in the context of large-scale
intrusion detection datasets, which may cause an SVM to become inefficient under …