# Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems
## 3 Related Work
### Related Research Using the Linsw-NBi5 Dataset  
This section discusses the research papers which used the UNSW-NB15 dataset for training and testing of the model.  
In the research by [PERSON] et al. [33], the authors proposed a network intrusion detection system based on an artificial neural network, trained and tested on the UNSW-NB15 dataset. The authors used deep learning in combination with other machine learning algorithms to extract the most relevant features of the dataset and use them for training the model. The goal was to increase the detection accuracy and decrease the False Alarm Rate. During the feature extraction phase, the authors used a combination of the Random Forest and the Decision Tree algorithms for feature extraction. In the end, they selected four features out of 45 in the original dataset. The authors have decided to do only binary classification, meaning that the model will only classify a record as an attack or regular traffic. The accuracy obtained in the testing phase was 89%, which is still lower than the accuracy of other proposals with deep learning approaches.  
[PERSON] et al. [34] have implemented an intrusion detection system based on a convolutional neural network (CNN). The goal was to make a model that would overtake the existing machine learning models used for intrusion detection concerning detection accuracy. The proposed algorithm is a CNN used for binary classification, with an accuracy of 93.5%.  
The research done by [PERSON] et al. [35] uses the whole dataset, with all 2,540,044 records, instead of the separate training and testing datasets prepared by the authors of the UNSW-NB15 dataset. The proposed model is a deep artificial neural network consisting of five hidden layers and a total of 50 neurons. The neural network is feed-forward and uses backpropagation and stochastic gradient descent. The research aimed to find the optimal network hyperparameters to achieve the best performance for binary classification. The authors conducted experiments to find the best activation function for their model and the optimal features to be used for training. The activation function that proved optimal for this research was the rectifier function without the dropout method. The second experiment regarding the optimal features showed that using the top 20% features, which were selected during feature extraction, gave the best results. After testing the proposed model, the evaluation showed high accuracy (98.99%) and a low false alarm rate (0.56%).  
We can note that few researchers, from the ones mentioned in this section, included the FPR and FNR as an evaluation metric in their research. However, most of them focused on calculating the accuracy. The main problem with this approach is that the datasets used are significantly unbalanced. Therefore the accuracy is not a good metric because it does not distinguish between the records of different classes that were correctly classified. With this concern in mind, in this paper we propose to focus on lowering the FNR and increasing the predictability for the minority classes.