@article {28071,
	title = {Federated Learning empowered End-Edge-Cloud Cooperation for 5G HetNet Security},
	journal = {IEEE Network},
	volume = {35},
	year = {2021},
	month = {03/2021},
	pages = {88-94},
	publisher = { IEEE},
	abstract = {The distributed and heterogeneous framework in the 5G heterogeneous network (Het-Net) makes it vulnerable to attacks of different kinds. Nodes for improving the network security are therefore important to eliminate such critical threats. Without cooperation or with limited cooperation, these nodes are substantially restricted in their protecting capacity due to specific characteristics such as heterogeneity, hierarchy, and wide range in the 5G HetNet. In this article, we propose a federated learning empowered end-edge-cloud cooperation-based framework for enhancing 5G HetNet security. In this framework, nodes equipped with attack detection mechanisms are distributed in the end, edge, and cloud of the 5G HetNet. We then design cooperative training schemes to realize the full potential of these nodes in detecting attacks. Illustrative results demonstrate the superior performance of our proposed scheme compared to three different benchmark schemes.},
	author = {Wei, Yunkai and Zhou, Sipei and Leng, Supeng and Maharjan, Sabita and Zhang, Yan}
}
@article {28065,
	title = {Protecting Machine Learning Integrity in Distributed Big Data Networking},
	journal = {IEEE Network},
	volume = {34},
	year = {2020},
	month = {07/2020},
	publisher = { IEEE},
	abstract = {A distributed big data network is the integration of big data and the underlying distributed network. This emerging paradigm brings the potential to divide big data processing tasks into smaller ones so that they can be intelligently processed in parallel with machine learning based on distributed network resources. Such a pattern requires strict system integrity, especially machine learning integrity against data tampering or network control by malicious nodes. In this article, we propose a secure architecture consisting of one HaSi scheme and two data tampering detection schemes for protecting the machine learning integrity in distributed big data networking. Illustrative results demonstrate the effect of our proposed schemes, and show that they can ensure the learning accuracy even when 30-40 percent of processing nodes are maliciously controlled. When the figure raises to 40-50 percent, the accuracy of our proposed schemes begins to fall visibly, but still outperforms the scenario without protection by up to 70-80 percent.},
	author = {Wei, Yunkai and Chen, Yijin and Xiao, Mingyue and Maharjan, Sabita and Zhang, Yan}
}
