IoT-23: A labeled dataset with malicious and benign IoT network traffic

By Sebastián García, Agustin Parmisano, María José Erquiaga. 2020

475 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/7356e0d60e76c18566850980a1000ba041f3f6e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Gérôme Bovet
2. Ernesto Damiani
3. Pedro Miguel S'anchez S'anchez
4. Gregorio Mart'inez P'erez
5. Sergio L'opez Bernal
6. Mario Quiles Pérez
7. Enrique Tomás Martínez Beltrán
8. Zhibo Zhang

Citations in the corpus

• Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, background): “[96] IoT-23 2020 381 IoT-23 is a dataset of Internet of Things (IoT) device network traffic.”
• Federated Learning for Malware Detection in IoT Devices (2021, unknown): “Finally, IoT23 [24] is a dataset consisting of 20 captures that include malware activity as well as 3 captures of benign IoT traffic.”
• Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, methodology): “Another study, conducted by Lian and Su [179], discussed a similar approach with the IoT23 dataset [180], obtaining a model convergence 30% faster than in the previous study with an increase of only 3% in the overhead”