UOS_IOTSH_2024: A Comprehensive network traffic dataset for sinkhole attacks in diverse RPL IoT networks.
| Autor: | Omar AARA; Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, P.O.Box 27272, Sharjah, United Arab Emirates., Soudan B; Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, P.O.Box 27272, Sharjah, United Arab Emirates., Altaweel A; Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, P.O.Box 27272, Sharjah, United Arab Emirates. |
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| Jazyk: | angličtina |
| Zdroj: | Data in brief [Data Brief] 2024 Jun 22; Vol. 55, pp. 110650. Date of Electronic Publication: 2024 Jun 22 (Print Publication: 2024). |
| DOI: | 10.1016/j.dib.2024.110650 |
| Abstrakt: | The proliferation of Internet of Things (IoT) implementations has enabled significant advancements across various applications, from smart homes to industrial automation. IoT networks typically consist of wirelessly interconnected, resource-constrained heterogeneous nodes. They are usually built using the energy-efficient Low Power and Lossy Network (LLN) standard, and employ the Routing Protocol for Low-Power and Lossy Networks (RPL) due to its efficiency in accommodating the constraints of IoT devices. However, RPL-based networks are susceptible to various security attacks that target the organization of the network. Chief among these is the sinkhole attack, which disrupts the network topology to attract traffic towards the malicious node by advertising false routing information. This work addresses the challenge of detecting sinkhole attacks on RPL-based IoT networks by introducing the extensive UOS_IOTSH_2024 dataset. This dataset is comprised mainly of raw network traffic collected through simulations of realistic IoT networks using the COOJA simulator. The dataset contains samples representing single and dual attackers in small and medium-sized IoT networks. It also covers both single-DODAG and dual-DODAG network architectures, as well as attackers at various locations across different topological positions for each scenario. In total, the dataset comprises 1,771,880 samples covering 60 different scenarios. (© 2024 The Author(s).) |
| Databáze: | MEDLINE |
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