GRASEC-IoT

Citation Author(s):
Djameleddine
Hamouche
ESI Algiers, Algeria
Reda
Kadri
ESI Algiers, Algeria
Mohamed-Lamine
Messai
Univ Lyon 2, UR ERIC, France
Hamida
Seba
UCBL, CNRS, LIRIS, UMR5205 F-69622 Villeurbanne, France
Submitted by:
Mohamed-Lamine ...
Last updated:
Tue, 08/27/2024 - 11:30
DOI:
10.21227/s97g-hn57
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Abstract 

The security of Internet of Things (IoT) networks has become a major concern in recent years, as the number of connected objects continues to grow, thereby opening up more potential for malicious attacks. Supervised Machine Learning (ML) algorithms, which require a labeled dataset for training, are increasingly employed to detect attacks in IoT networks. However, existing datasets focus only on specific types of attacks, resulting in ML-based solutions that struggle to generalize effectively. In this work, we address this limitation by introducing a new dataset that comprehensively covers most known attacks on IoT networks. We present GRASEC-IoT, a graph-based dataset specifically tailored for IoT networks, which provides structural information on attack patterns. This enables the utilization of Graph Neural Networks (GNNs), which have shown remarkable effectiveness across various domains. The dataset, its environment and scripts of attacks are publicly available [ https://gitlab.liris.cnrs.fr/gladis/grasec-iot ].

Funding Agency: 
the French National Research Agency
Data Descriptor Article DOI: 

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Submitted by Mohamed-Lamine ... on Tue, 08/27/2024 - 11:31

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