TY  - JOUR
AV  - none
EP  - 132
SN  - 1865-0929
JF  - Communications in Computer and Information Science
A1  - Babbar, Himanshi
A1  - Rani, Shalli
A1  - Singh, Aman
A1  - Gianini, Gabriele
SP  - 120
UR  - http://doi.org/10.1007/978-3-031-51643-6_9
TI  - Detecting Cyberattacks to Federated Learning on Software-Defined Networks
Y1  - 2024/02//
N2  - 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.
ID  - uninimx12560
VL  - 2022
ER  -