CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment

• DOI: 10.20944/preprints202305.0443.v1
• Publication Date: 2023-05-09T00:35:46Z
• Authors: Euclides Carlos P...; Sajjad Dadkhah; Raphael Ferreira; Alireza Zohourian; Rongxing Lu; Ali A Ghorbani
• Keywords: Chemical technology; Internet of Things; deep learning; Transportation; security; TP1-1185; Internet of Things (IoT); dataset; security; machine learning; deep learning; DoS; DDoS; reconnaissance; web attacks; brute force; spoofing; Mirai; Article; Internet of Things (IoT); Benchmarking; machine learning; dataset; Industry; DoS; Head

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, despite these benefits, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, standards, and server technologies). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, Brute Force, Spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices.