## 2 Related Work  
In an effort to arm on the threat of future unknown cyber-attacks, considerable work has gone into investigating and growing intrusion detection systems to help filter out associated malware, exploits, and vulnerabilities. Since the early 2000s, there have been many successful applications that incorporated data mining (DM) and machine learning (ML) methods for the IDS. Since then, many ML and DM algorithms were specifically designed for the purpose. DM algorithms examine the valuable information within large volume of data by analytically discovering underlying major trends, patterns, and associations from the data as reported in [10; 11]. Therefore, an artificial neural network (ANN) can be used to solve multiclass problems for the IDS using a classic multilayer feed-forward NN trained with a back-propagation algorithm to predict intrusions [12]. Many other (ML) methods have been used in IDS domains, such as Decision Trees, SVM and Random Forests [13]. The algorithms have estimated the performance of a set of pattern recognition, however the performance shows that certain classification algorithms are specifically efficient for a given attacks category while others lag behind. Moreover, they have also suggested a multiclassifier model for IDS [14] employed by the Random Forests technique in NIDSs (network intrusion detection systems). Furthermore, [PERSON] et al. [15] conducted an empirical investigation on independent hybrid data mining algorithms to improve the classification accuracy of Decision Tree and Naive Bayes for multiclass problems. [PERSON] and [PERSON] [16] proposed a framework of a network intrusion detection system based on data mining algorithm via the KDD'99 dataset; the research study claim that hidden naive Bayes (HNB) model can be applied to IDS that involves a variety of issues such as correlated features and high data stream volume. Nowadays, in the domain of network security (IDS), machine learning (ML), data mining (DM), and feature selection (FS) performs significant roles because many researchers are working on that domain to improve the performance of learning algorithms before applying in the different fields such as text mining, computer vision, and image processing etc. [17]. Feature selection is usually used for many reasons such as increased efficiency of the learning algorithm, achieving a high accuracy rate and getting easiness for classification problems [18]. Moreover, FS determines appropriate subset from the original dataset in order to minimize the impact of irrelevant and redundant features without greatly decreasing the accuracy of the classifier. However, several issues that remain to be addressed in feature selection; first, redundancy in datasets and combination process for FS techniques through any learning algorithm; second, an isolation and redundant feature selection as well as ineffective features selection from datasets--these problems lead to difficult stages for any learning algorithm. Furthermore, the Very Fast Decision Tree (VFDT) based on Hoeffing tree [19], uses information gain or gaini index for feature selection; it includes many refinement processes while training the model. The VFDT separates the features that are not promising before training the model. Though the accuracy of VFDT is low, the computational cost is high as well as error rate is high due to the complexity of tree. The most notable feature selection techniques such as, filter: applied as a ranker to rank all features without any classifier and wrapper: that uses a classifier to examine the features first [20]. The wrapper contributes good performance in small sets and the filter technique less expensive from a computational point of view [21]. Although, the association between feature selection and algorithm is not yet well managed, there is a need to be filled. Therefore, the regularized learning algorithmssuch as SVM [22], boosting [23], and sparse logistic regression (SPLR) have had a powerful impact in the domain of feature selection and classification problems.