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Published in SECRYPT, International Conference on Security and Cryptography, 2026
Position paper for SECRYPT 2026. This paper investigates the robustness of Graph Neural Network-based Network Intrusion Detection Systems (GNN-NIDS) against adversarial flow injection attacks. We propose a novel attack strategy that leverages the structural properties of network traffic graphs to evade detection. Our evaluation demonstrates the effectiveness of the proposed attack and highlights the need for improved defense mechanisms in GNN-NIDS.
Recommended citation: Mouzaoui, M.; Han, Y.; Blanc, G.; Rilling, G. and Hurfin, M. (2026). Robustness Evaluation of Graph Neural Network-Based Network Intrusion Detection Systems against Adversarial Flow Injection. In Proceedings of the 23rd International Conference on Security and Cryptography - Volume 1, ISBN 978-989-758-858-7, ISSN 2184-7711, pages 860-867.
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Published in ESORICS, European Symposium on Research in Computer Security, 2026
While Graph Neural Network (GNN)-based Network Intrusion Detection Systems (NIDS) excel by encoding relational traffic struc- tures, this structural dependency introduces novel adversarial vulner- abilities. Existing graph attacks remain impractical, often demanding white-box access, prohibitive query budgets, or exploit-breaking payload manipulations. To overcome these limitations, we propose a practical, hard-label black-box attack that evades detection strictly by injecting valid supplementary traffic flows, perfectly preserving the exploit’s func- tional integrity. By formulating this combinatorial challenge as a weakly submodular maximization problem, we leverage a local surrogate GNN and an influence-guided greedy search to efficiently identify highly impactful injections. We theoretically prove this approach yields a strict polynomial-time approximation guarantee. Empirically, we demonstrate the proposed flow injection attack can bypass state-of-the-art GNN-based NIDS with minimal query budgets
Recommended citation: Mouzaoui, M., Han, Y., Blanc, G., Rilling, G., Hurfin, M. (2027). Breaking GNN-Based NIDS via Hard-Label Black-Box Flow Injection Attacks. In: Spognardi, A., Sun, K., De Gaspari, F., Mancini, L.V. (eds) Computer Security – ESORICS 2026. ESORICS 2026. Lecture Notes in Computer Science, vol 16970. Springer, Cham. https://doi.org/10.1007/978-3-032-38702-8_13
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Published:
This paper investigates the robustness of Graph Neural Network-based Network Intrusion Detection Systems (GNN-NIDS) against adversarial flow injection attacks. We propose a novel attack strategy that leverages the structural properties of network traffic graphs to evade detection. Our evaluation demonstrates the effectiveness of the proposed attack and highlights the need for improved defense mechanisms in GNN-NIDS.
Published:
While Graph Neural Network (GNN)-based Network Intrusion Detection Systems (NIDS) excel by encoding relational traffic struc- tures, this structural dependency introduces novel adversarial vulner- abilities. Existing graph attacks remain impractical, often demanding white-box access, prohibitive query budgets, or exploit-breaking payload manipulations. To overcome these limitations, we propose a practical, hard-label black-box attack that evades detection strictly by injecting valid supplementary traffic flows, perfectly preserving the exploit’s func- tional integrity. By formulating this combinatorial challenge as a weakly submodular maximization problem, we leverage a local surrogate GNN and an influence-guided greedy search to efficiently identify highly impactful injections. We theoretically prove this approach yields a strict polynomial-time approximation guarantee. Empirically, we demonstrate the proposed flow injection attack can bypass state-of-the-art GNN-based NIDS with minimal query budgets
Published:
Presentation of the paper “Breaking GNN-based NIDS via Hard-Label Black-Box Flow Injection Attacks” at the PTTC (Programme de Transfer du Campus Cyber) on September 24, 2026. The talk was presented for an mixed audience of researchers and industry professionals.
Graduate course, CentraleSupelec, 2024
Teaching assistant for practical sessions for students in their final year of Master’s.
Graduate courses, ESIR, Université de Rennes, 2024
Teaching assistant for master’s students. The module is about Database Management and Python programming. The students learn how to design a database and perform queries on it.
Graduate course, CentraleSupelec, 2025
Teaching assistant for practical sessions for students in their final year of Master’s (cyber & infosec).
Graduate courses, ESIR, Université de Rennes, 2025
Teaching assistant for master’s students. Module about Database Management and Python programming.