A A Comparative Study on Standard Datasets for Intrusion Detection in Internet of Things Networks (Sebha University Conference Proceedings, 2024)
In recent years, the widespread use of Internet of Things (IoT) applications has contributed to the development of smart cities, but with the growth of smart city networks, the risk of cybersecurity threats and attacks increases. Despite the spread of many security mechanisms such as encryption technologies and firewalls, it is impossible to avoid various attacks on IoT networks. To address this problem, machine learning has been used as an effective tool to detect attacks. This is done by applying a number of supervised classification algorithms to a dataset. This study reviews some of the common datasets for detecting intrusions into networks in general and IoT networks in particular, the most important of which are: KDD Cup '99, Kyoto2006+, NSL-KDD, UNSW-NB15, CIC-IDS 2017, CSE-CIC-IDS 2018. In addition to comparing them based on the number of features in each, the presence of recent attacks, the total number of records and the number of attack categories. At the end of this paper, the most important previous studies that dealt with the application of some machine learning algorithms on the data set under study were reviewed and the performance indicators were summarized, including accuracy and training time of the algorithms.