# A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector
## 2 Related Work  
The authors in reference [3] developed a nonsymmetric deep autoencoder (NDAE) to extract features and utilized an RFC for classification purposes. Their NDAE successfully reduced the initial 41 input features to 28. The experiments were conducted on the KDDCup99 and NSL-KDD datasets. In the case of five-class classification, the KDDCup99 dataset yielded an accuracy of 97.8%, while the NSL-KDD dataset achieved 85.4% accuracy. In our research, we also employed the NSL-KDD dataset, but we utilized a deep learning approach for classification. As a result, we were able to attain a higher accuracy using a smaller feature vector. In [4], the researchers employed the KDDCup99 dataset, which contained 41 input features. They proposed an LSTM-RNN classification model. The experimental process involved two stages, firstly, determining the best values for the hyperparameters and, secondly, assessing the classification performance using these chosen hyperparameters. The authors conducted five-class classifications and achieved an accuracy of 96.9%. Thisresearch successfully leveraged the capabilities of deep learning approaches to achieve a high level of accuracy. In [5], the authors conducted an evaluation of five distinct datasets utilizing six different classifiers, namely: Naive Bayes, decision trees, random forests, neural networks (multilayer perceptron), support vector machines (RBF kernel), and l2-logistic regression. The random forests classifier yielded the best performance among the classifiers assessed. The authors only provided the results for this particular classifier. For feature selection, the authors employed the decision tree algorithm, and for feature extraction, they utilized principal component analysis. However, the authors did not specify the final size of the input vector after transformation into principal components. On the UNSW-NB15 dataset, the authors reported a test accuracy of 98.9%. It is worth noting that this research achieved a higher accuracy than our own. Nonetheless, it is important to highlight that the authors ended up with 32 features after performing feature selection, out of a total of 41 features, which is a significantly larger number than in our study, where only nine features were retained. The authors in [7] employed both the NSL-KDD and UNSW-NB15 datasets in their study. They utilized several models for network intrusion detection, namely support vector machine, multilayer perceptron, restricted Boltzman machine, sparse autoencoder, and wide and deep learning. They used all features. The results revealed that the sparse autoencoder model achieved a better accuracy of 79.3% on the NSL-KDD dataset, while the wide and deep network models attained a higher accuracy of 91.2% on the UNSW-NB15 dataset. Despite using a smaller feature vector in both cases, our technique demonstrated superior performance. In [8], self-taught and deep learning approaches were based on the NSL-KDD dataset. The approach used consisted of two stages, where feature learning was implemented in the first stage and classification in the second stage. The first stage used unlabeled data, whilst labelled data were used in the classification stage. Sparse autoencoder was applied for feature learning, and softmax regression was employed for classification. This approach achieved 88.39% test accuracy on two-class data. Notably, we achieved an even higher accuracy metric on the same dataset by employing a smaller feature vector. The authors in [9] utilized all the features available in the UNSW-NB15 and KDD99 datasets for classification purposes. They employed five different classifiers: Naive Bayes, decision trees, artificial neural networks, logistic regression, and expectation-maximization. Although the authors did not mention the number of classes classified, the inclusion of logistic regression suggests that a two-class classification was performed. On the UNSW-NB15 dataset, they achieved the highest classification accuracy of 85.5% using the decision trees algorithm.