CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment

被引:65
|
作者
Neto, Euclides Carlos Pinto [1 ]
Dadkhah, Sajjad [1 ]
Ferreira, Raphael [1 ]
Zohourian, Alireza [1 ]
Lu, Rongxing [1 ]
Ghorbani, Ali A. [1 ]
机构
[1] Univ New Brunswick UnB, Fac Comp Sci, Fredericton, NB E3B 5A3, Canada
关键词
Internet of Things (IoT); dataset; security; machine learning; deep learning; DoS; DDoS; reconnaissance; web attacks; brute force; spoofing; Mirai; INTERNET; THINGS; CHALLENGES; SYSTEM;
D O I
10.3390/s23135941
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of 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, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). 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. The dataset is available on the CIC Dataset website.
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页数:26
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