%0 Conference Proceedings
%T Network Anomaly Detection Using Federated Deep Autoencoding Gaussian Mixture Model
%+ Nanyang Technological University [Singapour]
%+ School of Computer Science and Engineering (SCSE)
%A Chen, Yang
%A Zhang, Junzhe
%A Yeo, Chai, Kiat
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 2nd International Conference on Machine Learning for Networking (MLN)
%C Paris, France
%Y Selma Boumerdassi
%Y Éric Renault
%Y Paul Mühlethaler
%I Springer International Publishing
%3 Machine Learning for Networking
%V LNCS-12081
%P 1-14
%8 2019-12-03
%D 2019
%R 10.1007/978-3-030-45778-5_1
%K Anomaly detection
%K Small dataset
%K Privacy-preserving
%K Federated learning
%K Deep autoencoding Gaussian mixture model
%K Network security
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X Deep autoencoding Gaussian mixture model (DAGMM) employs dimensionality reduction and density estimation and jointly optimizes them for unsupervised anomaly detection tasks. However, the absence of large amount of training data greatly compromises DAGMM’s performance. Due to rising concerns for privacy, a worse situation can be expected. By aggregating only parameters from local training on clients for obtaining knowledge from more private data, federated learning is proposed to enhance model performance. Meanwhile, privacy is properly protected. Inspired by the aforementioned, this paper presents a federated deep autoencoding Gaussian mixture model (FDAGMM) to improve the disappointing performance of DAGMM caused by limited data amount. The superiority of our proposed FDAGMM is empirically demonstrated with extensive experiments.
%G English
%Z TC 6
%2 https://inria.hal.science/hal-03266470/document
%2 https://inria.hal.science/hal-03266470/file/487577_1_En_1_Chapter.pdf
%L hal-03266470
%U https://inria.hal.science/hal-03266470
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC6
%~ IFIP-LNCS-12081
%~ IFIP-MLN