Deep Feature Learning

FL is a technique that models the behavior of data from a subset of attributes only. It also shows the correlation between detection performance and traffic model quality efficiently (Palmieri et al., Concurrency Comput Pract Exp 26(5):1113–1129, 2014). However, feature extraction and feature selection are different. Feature extraction algorithms derive new features from the original features to (i) reduce the cost of feature measurement, (ii) increase classifier efficiency, and (iii) improve classification accuracy, whereas feature selection algorithms select no more than m features from a total of M input features, where m is smaller than M. Thus, the newly generated features were merely selected from the original features without any transformation. However, their goal is to derive or select a characteristic feature vector with a lower dimensionality which is used for the classification task. One advantage of deep learning models is processing underlying data from the input which suits for FL tasks. Therefore, we discuss this critical role of deep learning in IDS as Deep Feature Extraction and Selection (D-FES) and deep learning for clustering.