## 1 Introduction
### Machine and Deep Learning Using IDS  
IDSs play a significant role in cybersecurity as they monitor the network to protect it from cyber threats. Following are a few frequently used methods in the research conducted: Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANN), Self-Organizing Map (SOM), etc.  
An up-to-date and elaborated dataset including numerous features and criteria was essentially required by the researchers. They were required during experiments, evaluation, and model testing on the given modern networks [13; 14]. Not all datasets provided in the experiments deliver all the features provided in the literature. So thus only a few well-known datasets are referred to.  
Network attacks have become common nowadays due to which datasets are continuously evaluated. The KDD 99 [15], CAIDA [16], ISCX2012 [17], and KYOTO [18] are a few datasets that were frequently used as they represented real-world network traffic. Despite their use, they are now outdated. Currently, the CICIDS2017 dataset is widely used. It is commonly used in the field of research. One of the main reasons for its popularity is that it was introduced to overcome the problems noticed in previous datasets [9] apart from this advantage it also holds numerous traffic types, furthermore, it also contains real-world network traffic. Although CICIDS2017 is an appropriate dataset there is a major issue that needs to be resolved. The consequences of this issue leads to high-class imbalance which directly misleads the classifier [13]. Although a new dataset named CIS AWS 2018 [19] has appeared, which is quite similar to CICIDS2017, its previous version is not reported yet. In this research we detect two different attacks; active and passive where DoS Hulk, DoS GoldenEye, and DDoS belong to the active attacks while PortScan belongs to the passive attacks. A detailed description of the attacks and their types has been given in Table 1.  
In this study, our objective is to develop an anomalous network intrusion system based on flow-based statistics, using the CICIDS2017 dataset which detects and classifies every form of assault in a multi-categorization utilizing a high degree of accuracy. Deep learning approaches in the field of machine learning were used to accomplish flow categorization. Great success has been noted with the use of deep learning. In this research we used one dimensional convolutional neural networks (1D CNN), which works quite well on textual data in order to identify network intrusions and classify various types of threats. To identify network traffic, we created an enhanced deep neural network model based on 1D CNN. As a result, this study proposes a flow-based anomaly detection system based on deep learning. Furthermore, the main contributions are described below:  
* Proposed an efficient complete system for NIDS using deep learning-based approach.
* Presented a comprehensive analysis of existing techniques based on machine learning and state-of-the-art deep learning.
* In-depth analysis of the CICIDS2017 dataset.
* Presented a detailed comparison with existing Network Intrusion Detection Systems
* Proposed one-dimensional convolution neural network-based multi-class classification technique.  
The structure of this paper is organized as follows. Section 2 analyzes most of the related work of network intrusion systems utilizing deep learning and machine learning models. Section 3 discusses the dataset and its pre-processing method. Section 4 presents our proposed work utilizing deep learning techniques for network intrusion detection. The outcomes and discussion for our model are discussed in Section 5. Finally, Section 6 summarizes the findings and provides a few recommendations for future research.