In the context of Information-Centric Networking, Interest Flooding Attacks (IFAs) represent a new and dangerous sort of distributed denial of service. Since existing proposals targeting IFAs mainly focus on local information, in this paper we propose GNN4IFA as the first mechanism exploiting complex non-local knowledge for IFA detection by leveraging Graph Neural Networks (GNNs) handling the overall network topology. In order to test GNN4IFA, we collect SPOTIFAI, a novel dataset filling the current lack of available IFA datasets by covering a variety of IFA setups, including ∼ 40 heterogeneous scenarios over three network topologies. We show that GNN4IFA performs well on all tested topologies and setups, reaching over 99% detection rate along with a negligible false positive rate and small computational costs. Overall, GNN4IFA overcomes state-of-the-art detection mechanisms both in terms of raw detection and flexibility, and – unlike all previous solutions in the literature – also enables the transfer of its detection on network topologies different from the one used in its design phase.

GNN4IFA: Interest Flooding Attack Detection with Graph Neural Networks / Agiollo, Andrea; Bardhi, Enkeleda; Conti, Mauro; Lazzeretti, Riccardo; Losiouk, Eleonora; Omicini, Andrea. - (2023), pp. 615-630. [10.1109/EuroSP57164.2023.00043].

GNN4IFA: Interest Flooding Attack Detection with Graph Neural Networks

Enkeleda Bardhi
;
Mauro Conti;Riccardo Lazzeretti;
2023

Abstract

In the context of Information-Centric Networking, Interest Flooding Attacks (IFAs) represent a new and dangerous sort of distributed denial of service. Since existing proposals targeting IFAs mainly focus on local information, in this paper we propose GNN4IFA as the first mechanism exploiting complex non-local knowledge for IFA detection by leveraging Graph Neural Networks (GNNs) handling the overall network topology. In order to test GNN4IFA, we collect SPOTIFAI, a novel dataset filling the current lack of available IFA datasets by covering a variety of IFA setups, including ∼ 40 heterogeneous scenarios over three network topologies. We show that GNN4IFA performs well on all tested topologies and setups, reaching over 99% detection rate along with a negligible false positive rate and small computational costs. Overall, GNN4IFA overcomes state-of-the-art detection mechanisms both in terms of raw detection and flexibility, and – unlike all previous solutions in the literature – also enables the transfer of its detection on network topologies different from the one used in its design phase.
2023
IEEE 8th European Symposium on Security and Privacy (EuroS&P)
978-1-6654-6512-0
Network Security; Interest Flooding Attacks; Graph Neural Networks; Emerging Networks
02 Pubblicazione su volume::02a Capitolo o Articolo
GNN4IFA: Interest Flooding Attack Detection with Graph Neural Networks / Agiollo, Andrea; Bardhi, Enkeleda; Conti, Mauro; Lazzeretti, Riccardo; Losiouk, Eleonora; Omicini, Andrea. - (2023), pp. 615-630. [10.1109/EuroSP57164.2023.00043].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1686129
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