# A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector
## 2 Related Work
However, our research achieved even higher accuracy using a smaller feature vector. The work in [10] covered flow-based network anomaly detection in a Software Defined Networking (SDN) environment using the NSL-KDD dataset. Six flow features were used from a total of 41 features. Here, a feedforward neural network with three hidden layers was used for classification. The model was referred to as a Deep Neural Network (DNN). They reported 75.75% test accuracy when a 0.001 learning rate was applied. While it is worth noting that our achieved result surpassed theirs, it is important to consider that they employed a smaller number of features in their analysis, which may not have adequately generalized the training set. Furthermore, the sole criterion for selecting these features was that they were flow-based features. Reference [11] used six different datasets. We discuss this paper with reference to the UNSW-NB and NSL-KDD datasets. The authors designed a multilayer perceptron feedforward network for classification and proposed a Deep Neural Network (DNN) architecture. All the features of the datasets were used for the classification. The authors reported an accuracy of 78.4% on the UNSW-NB15 dataset and 80.1% on the NSL-KDD dataset. It is important to note that these accuracies were lower than those achieved by our approach. We present a comparison table of their results with our results later in Section 6. In the study [12], all 41 features of the NSL-KDD dataset were utilized. However, after data preprocessing, the number of features increased to 122, resulting in an input vector size of 122. To classify the anomalies, a convolutional neural network with two hidden layers was employed. The classification task was performed on different versions of the NSL-KDD dataset. The highest achieved accuracy of 79.48% was observed on the KDDTest+ dataset, which was lower than the accuracy we achieved using a smaller feature vector in our research. Additionally, the authors proposed an approach to address the issue of data imbalance. Although valuable, this approach was not directly related to our work; therefore, it is not discussed in this study. Reference [13] conducted feature selection on three datasets: KDDCup99, UNSW-NB15, and NSL-KDD. They employed the fruit fly algorithm, the ant lion optimizer algorithm, and a hybrid version for selecting the most relevant features. The selected features were then evaluated using SVM, KNN, Naive Bayes, and decision tree classifiers. In our work, we focused on the fruit fly feature selection method and utilized the features identified by this algorithm. Compared to the other two algorithms, the fruit fly algorithm selected the smallest number of features, making it suitable for our study. The authors of reference [13] reported achieving the highest accuracy of 89.0% using the k-nearest neighbor classifier on the NSL-KDD dataset and 90.6% accuracy using the decision tree algorithm on the UNSW-NB15 dataset, both utilizing the features selected by the fruit fly algorithm. However, it should be noted that our research achieved even higher accuracy by employing a deep learning algorithm, whereas the referenced research used shallow learning algorithms. The authors in reference [14] employed five variants of autoencoders to detect network traffic anomalies in the UNSW-NB15 and NSL-KDD datasets. In this unsupervised approach, the authors leveraged the power of deep learning to learn the compressed representation. On the NSL-KDD test dataset, they achieved an accuracy of 87.9% using the contractive autoencoder, while on the UNSW-NB15 dataset, they achieved an accuracy of 86.6% using the convolutional autoencoder. In both cases, our approach yielded better results than this approach, suggesting that relying solely on deep learning approaches for feature reduction was insufficient. It is better to employ other feature selection techniques to remove unnecessary features before inputting them into the deep learning model.