Dans le cadre de notre séminaire « La Cybersécurité sur un plateau » (Cybersecurity on a Plate), nous aurons 1 présentation le mardi 14 janvier prochain. Le séminaire CoaP aura lieu à 10h30 dans le bâtiment IMT/TP/TSP, en salle 3.A213.

Si vous venez participer pour la première fois, n'hésitez pas à contacter les organisateurs pour ne pas être bloqué à l'entrée.

Sara Chennoufi (Télécom SudParis) - Towards Interpretable and Resilient Cyber Intrusion Detection in Heterogeneous Environments

Abstract : In distributed networks, devices face diverse cyberattacks, highlighting the need for collaborative mechanisms like Federated Learning (FL) to achieve a global knowledge of various attack types. FL is a privacy-preserving machine learning paradigm that enables collaborative model training without sharing sensitive data. However, the effectiveness of FL is often hindered by the heterogeneity of attack data across different networks known as non-identically distributed (non-IID) data. To address these challenges, we propose Argos, a Federated Prototype Learning (FPL) framework designed to improve collaborative and interpretable detection of network attacks in heterogeneous environments. FPL enables the sharing of class-specific prototypes, facilitating the exchange of interpretable knowledge and improving the detection performance for individual classes. Additionally, we analyze the privacy risks associated with prototype sharing and investigate their effectiveness in identifying mislabeled data.

Biography : Sara Chennoufi is a PhD student at Télécom SudParis, working on the development of intrusion detection systems for 5G networks. Her research focuses on enhancing privacy-preserving distributed systems using Federated Learning, addressing key challenges in 5G such as system heterogeneity and the rapid spread of new cyber attacks. She graduated from the Higher School of Computer Science (ESI Algiers). For her graduation project, she completed an internship at INSA Lyon, where she also explored Federated Learning and poisoning attacks.