# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
## 2 Background and Related Works
### Related Works
#### 2.2.2 Recent Public Datasets  
Recently, a few newer public datasets available based on network intrusion detection have been introduced. One of them is the Bogazici University (BOUN DDoS) dataset, which is of resource-depletion DDoS attacks [52]. It was generated and recorded from router backbone mirrored ports at the Bogazici University campus environment. The datasets include non-attack and attack traffic such as TCP SYN, and UDP flooding packets based on Hping3 traffic generator software by flooding. There are some advantages of BOUN DDoS datasets. It provides simple resource depletion-type DDoS attacks on a campus network, which are suitable for generating and analyzing network-based attack detection methods. It consists of different intensities of attacks to help researchers to train and estimate their IDS methodologies for different attack densities. BOUN dataset consists of genuine background internet traffic combined with DDoS attack traffic. These datasets provide easier simulation and analysis because of small file sizes and fewer packets compared to other datasets, which helps researchers to import datasets in different research software platforms easily. However, BOUN DDoS dataset has limitations when it comes to achieving the task we are trying to solve in this paper. It only consists of only basic attack types such as DDoS attack, TCP SYN flood, UDP flood attack. BOUN DDoS has not been widely adopted by the research community as a benchmark dataset. The datasets have been used in academia but only by the authors who generated them.  
Similarly, the LITNET-2020 dataset is a new annotated network benchmark dataset that contains real-world network traffic data and under-attack data samples from the academic networks environment captured over 10 months [53]. It consists of 85 network flow features that can be used to recognize 12 network attacks. The dataset features were analyzed based on statistical analysis and clustering methods. Some advantages of these datasets are as follows: It contains real-world network traffics, unlike other datasets, which were generated synthetically. The datasets are freely available for research purpose and can be used to benchmark network intrusion datasets. The datasets were accumulatedover a longer period than other datasets, i.e., 10 months. It is therefore very helpful for researchers and academicians working in the cybersecurity domain. However, for the task we are trying to solve, they have some limitations. These datasets are new datasets that have not yet been widely adopted by the research community as benchmark datasets. For analysis, we used datasets that have been widely accepted, used, and analyzed by researchers and academicians such as CIC-IDS2018 datasets. The LITNET-2020 dataset lacks some of the popular attack types such as DDoS attacks, brute-force attacks, BoTnet, and infiltration attack types. Nevertheless, LITNET-2020 dataset might present an important contribution to the research community by enriching the number of datasets accessible for the development and refinement of new network-attack identification systems. This dataset has the potential to be adopted in new research for NIDS.