Paper published in a book (Scientific congresses, symposiums and conference proceedings)
Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks
Korba, Abdelaziz Amara; Boualouache, Abdelwahab; Bouziane, Brik et al.
2023In IEEE ICC 2023 - IEEE International Conference on Communications
Peer reviewed


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Keywords :
5G and Beyond; Connected and Automated Vehicles; Security; Zero-day Attacks; Federated learning
Abstract :
[en] Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning-based solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases the communication overhead, and raises privacy concerns. To address the aforementioned limits, we propose in this paper a novel detection mechanism that leverages the ability of the deep auto-encoder method to detect attacks relying only on the benign network traffic pattern. Using federated learning, the proposed intrusion detection system can be trained with large and diverse benign network traffic, while preserving the CAVs’ privacy, and minimizing the communication overhead. The in-depth experiment on a recent network traffic dataset shows that the proposed system achieved a high detection rate while minimizing the false positive rate, and the detection delay.
Disciplines :
Computer science
Author, co-author :
Korba, Abdelaziz Amara
Boualouache, Abdelwahab ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Bouziane, Brik;  University of Bourgogne, France > DRIVE
Rahal, Rabah;  Badji Mokhtar Annaba University, Algeria > LRS
Ghamri-Doudane, Yacine;  University of La Rochelle, France > L3I
Senouci, Sidi Mohammed;  University of Bourgogne > DRIVE
External co-authors :
Language :
Title :
Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks
Publication date :
Event name :
IEEE ICC 2023 - IEEE International Conference on Communications
Event date :
from 28-05-2023 to 01-06-2023
Audience :
Main work title :
IEEE ICC 2023 - IEEE International Conference on Communications
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
FnR Project :
FNR14891397 - Intelligent Orchestrated Security And Privacy-aware Slicing For 5g And Beyond Vehicular Networks, 2020 (01/04/2021-31/03/2024) - Thomas Engel
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since 17 August 2023


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