TY - GEN
T1 - GNN4IFA
T2 - 8th IEEE European Symposium on Security and Privacy, Euro S and P 2023
AU - Agiollo, Andrea
AU - Bardhi, Enkeleda
AU - Conti, Mauro
AU - Lazzeretti, Riccardo
AU - Losiouk, Eleonora
AU - Omicini, Andrea
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Emerging Networks
KW - Graph Neural Networks
KW - Interest Flooding Attacks
KW - Network Security
UR - http://www.scopus.com/inward/record.url?scp=85168119525&partnerID=8YFLogxK
U2 - 10.1109/EuroSP57164.2023.00043
DO - 10.1109/EuroSP57164.2023.00043
M3 - Conference contribution
AN - SCOPUS:85168119525
T3 - Proceedings - 8th IEEE European Symposium on Security and Privacy, Euro S and P 2023
SP - 615
EP - 630
BT - Proceedings - 8th IEEE European Symposium on Security and Privacy, Euro S and P 2023
PB - Institute of Electrical and Electronics Engineers (IEEE)
Y2 - 3 July 2023 through 7 July 2023
ER -