Andrea Agiollo, Enkeleda Bardhi, Mauro Conti, Riccardo Lazzeretti, Eleonora Losiouk,
Andrea Omicini
2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P), pages 615–630
IEEE Computer Society, Los Alamitos, CA, USA
July 2023
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.
reference talk
origin event
funding project
EXPECTATION — Personalized Explainable Artificial Intelligence for decentralized agents with heterogeneous knowledge
(01/04/2021–31/03/2024)
works as
reference publication for talk
GNN4IFA: Interest Flooding Attack Detection With Graph Neural Networks (EuroS&P 2023, 05/07/2023) — Andrea Agiollo, Enkeleda Bardhi (Andrea Agiollo, Enkeleda Bardhi, Mauro Conti, Riccardo Lazzeretti, Eleonora Losiouk, Andrea Omicini)