eprintid: 12560 rev_number: 6 eprint_status: archive userid: 2 dir: disk0/00/01/25/60 datestamp: 2024-06-07 23:30:11 lastmod: 2024-06-07 23:30:12 status_changed: 2024-06-07 23:30:11 type: article metadata_visibility: show creators_name: Babbar, Himanshi creators_name: Rani, Shalli creators_name: Singh, Aman creators_name: Gianini, Gabriele creators_id: creators_id: creators_id: aman.singh@uneatlantico.es creators_id: title: Detecting Cyberattacks to Federated Learning on Software-Defined Networks ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica full_text_status: none abstract: Federated learning is a distributed machine-learning technique that enables multiple devices to learn a shared model while keeping their local data private. The approach poses security challenges, such as model integrity, that must be addressed to ensure the reliability of the learned models. In this context, software-defined networking (SDN) can play a crucial role in improving the security of federated learning systems; indeed, it can provide centralized control and management of network resources, enforcement of security policies, and detection and mitigation of network-level threats. The integration of SDN with federated learning can help achieve a secure and efficient distributed learning environment. In this paper, an architecture is proposed to detect attacks on Federated Learning using SDN; furthermore, the machine learning model is deployed on a number of devices for training. The simulation results are carried out using the N-BaIoT dataset and training models such as Random Forest achieves 99.6%, Decision Tree achieves 99.8%, and K-Nearest Neighbor achieves 99.3% with 20 features. date: 2024-02 publication: Communications in Computer and Information Science volume: 2022 pagerange: 120-132 id_number: doi:10.1007/978-3-031-51643-6_9 refereed: TRUE issn: 1865-0929 book_title: Management of Digital EcoSystems official_url: http://doi.org/10.1007/978-3-031-51643-6_9 access: close language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Cerrado Inglés Federated learning is a distributed machine-learning technique that enables multiple devices to learn a shared model while keeping their local data private. The approach poses security challenges, such as model integrity, that must be addressed to ensure the reliability of the learned models. In this context, software-defined networking (SDN) can play a crucial role in improving the security of federated learning systems; indeed, it can provide centralized control and management of network resources, enforcement of security policies, and detection and mitigation of network-level threats. The integration of SDN with federated learning can help achieve a secure and efficient distributed learning environment. In this paper, an architecture is proposed to detect attacks on Federated Learning using SDN; furthermore, the machine learning model is deployed on a number of devices for training. The simulation results are carried out using the N-BaIoT dataset and training models such as Random Forest achieves 99.6%, Decision Tree achieves 99.8%, and K-Nearest Neighbor achieves 99.3% with 20 features. metadata Babbar, Himanshi; Rani, Shalli; Singh, Aman y Gianini, Gabriele mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR (2024) Detecting Cyberattacks to Federated Learning on Software-Defined Networks. Communications in Computer and Information Science, 2022. pp. 120-132. ISSN 1865-0929