## 3 Related Work
[13] studied the bit error rate performance of bi-quadrature physical-layer network coding (BQ-PNC) in asymmetric two-way relay channels and found that BQ-PNC can significantly improve bit error rate (BER) performance either in symmetric or asymmetric circumstances. Most real-world network traffic data are not available because of privacy and security issues, but several public datasets are available for IDS performance assessment. Some of these datasets suffer from a lack of adequate traffic types and up-to-date low-footprint attack styles, so researchers use an older benchmark dataset, NSL-KDD, to compare the performance of ML classifiers in a fair and reasonable manner. For example, [PERSON] and [PERSON] [14] used the NSL-KDD dataset to evaluate the performance and accuracy of five ML classifiers with or without feature reduction using the Weka tool. They reported that the NSL-KDD dataset is ideal for comparing different intrusion detection models and that the random forest method has the highest accuracy among J48, support vector machine (SVM), CART, and Naive Bayes. Using the Weka tool also, [PERSON] and [PERSON] [15] used 20% of the NSL-KDD dataset to measure the effectiveness of four ML classifiers (CFS, J48, SVM, and naive bayes (NB)) in detecting anomalies in a network traffic pattern. They concluded that the detection accuracy of J48 outperformed that of SVM and NB. [PERSON] et al. [16] evaluated the SVM classifier's performance when integrated with other classifiers, such as BayesNet, AdaBoost, logistic, IBK, J48, random forest, JRip, OneR, and simple CART, on the NSL-KDD dataset using the Weka tool. They concluded that a multi-classifier algorithm had better performance than a single-classifier algorithm. They showed that the stacking of SVM and random forest gave the best performance, with a detection accuracy rate of 97.50%. However, they did not report the time taken to build the model. [PERSON] and [PERSON] [17] proposed an ID model using chi-square feature selection and multi-class SVM. The main idea behind this model was to form a multi-class SVM to reduce the training and testing time and increase the classification accuracy of network attacks. They tested this model on the NSL-KDD and gurekddcup datasets and showed that the proposed method performed better; it had higher accuracy, faster convergence speed, and better generalization. [PERSON] and [PERSON] [18] studied and compared the performance of nine classifiers (BayesNet, logistic, IBK, J48, PART, JRip, random tree, random forest, and REPTree) over the training and testing datasets of NSL-KDD using 10-fold cross-validation in the Weka tool. They concluded that random forest and BayesNet were suitable for the proper detection of network intrusion. [PERSON] and [PERSON] [3] analyzed four supervised ML classifiers, namely SVM, random forest (RF), LR, and NB,for intrusion detection over the NSL-KDD dataset. They used accuracy, true positive rate (TPR), and FPR as the parameters to measure the performance of the classifiers, as well as an Intel Core (TM) i5-3230M CPU @ 2.60 GHz, 4 GB RAM to execute the experimental procedure. They found that the RF classifier outperformed the other tested classifiers in identifying whether data traffic was normal or was an attack. Next, [PERSON] [19] compared the performance of five classifiers (k-NN, DT, NN, SVM, and NB) using 5-fold cross-validation on the NSL-KDD dataset and concluded that the k-NN classifier showed better performance than the other classifiers. [PERSON] et al. [20] suggested an effective IDS based on SVM with augmented features. They applied logarithm marginal density ratio transformation to form new and better transformed features that can improve the detection performance of the SVM-based detection model. They used the NSL-KDD dataset to evaluate the suggested method and concluded that it achieves better performance than other existing methods in terms of accuracy, detection rate, false alarm rate, and training speed for the SVM model. [PERSON] et al.