Top

Published in:

2020 | OriginalPaper | Chapter

Network Anomaly Detection Using Federated Deep Autoencoding Gaussian Mixture Model

Authors : Yang Chen, Junzhe Zhang, Chai Kiat Yeo

Published in: Machine Learning for Networking

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

next chapter Towards a Hierarchical Deep Learning Approach for Intrusion Detection

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRef

Zhang, J., Yeung, S.H., Shu, Y., He, B., Wang, W.: Efficient memory management for GPU-based deep learning systems. arXiv preprint arXiv:1903.06631 (2019)

Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407 (2019)

Javaid, A., Niyaz, Q., Sun, W., Alam, M.: A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 21–26 (2016)

Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection (2018)

McMahan, H.B., Moore, E., Ramage, D., Hampson, S., et al.: Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)

Edgeworth, F.Y.: Xli. on discordant observations. Lond. Edinb. Dublin Philos. Mag. J. Sc. 23(143), 364–375 (1887)CrossRef

Tsai, C.F., Hsu, Y.F., Lin, C.Y., Lin, W.Y.: Intrusion detection by machine learning: a review. Expert Syst. Appl. 36(10), 11994–12000 (2009)CrossRef

M. LLC.: MS Windows NT kdd cup (1999). http://kdd.ics.uci.edu/databases/kddcup99/task.html

10.

Stolfo, S.J., Fan, W., Lee, W., Prodromidis, A., Chan, P.K.: Cost-based modeling for fraud and intrusion detection: results from the jam project. In: Proceedings DARPA Information Survivability Conference and Exposition, DISCEX 2000, vol. 2, pp. 130–144. IEEE (2000)

11.

Konečný, J., McMahan, B., Ramage, D.: Federated optimization: distributed optimization beyond the datacenter. arXiv Prepr arXiv:1511.03575, no. 1, pp. 1–5 (2015)

12.

Konecný, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. CoRR, vol. abs/1610.0, no. NIPS, pp. 1–5 (2016)

13.

Ma, C., et al.: Distributed optimization with arbitrary local solvers. Optim. Methods Softw. 32(4), 813–848 (2017)MathSciNetCrossRef

14.

Reddi, S.J., Konečnỳ, J., Richtárik, P., Póczós, B., Smola, A.: Aide: fast and communication efficient distributed optimization. arXiv preprint arXiv:1608.06879 (2016)

15.

Chen, Y., Sun, X., Jin, Y.: Communication-efficient federated deep learning with asynchronous model update and temporally weighted aggregation. arXiv preprint arXiv:1903.07424 (2019)

16.

House, W.: Consumer data privacy in a networked world: a framework for protecting privacy and promoting innovation in the global digital economy. White House, pp. 1–62. Washington, DC (2012)

17.

Chen, Y., Sun, X., Hu, Y.: Federated learning assisted interactive EDA with dual probabilistic models for personalized search. In: Tan, Y., Shi, Y., Niu, B. (eds.) ICSI 2019. LNCS, vol. 11655, pp. 374–383. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26369-0_35CrossRef

18.

McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017)

19.

Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)MathSciNetCrossRef

Title: Network Anomaly Detection Using Federated Deep Autoencoding Gaussian Mixture Model
Authors: Yang Chen
Junzhe Zhang
Chai Kiat Yeo
Publisher: Springer International Publishing
Book: Machine Learning for Networking
Print ISBN: 978-3-030-45777-8

Electronic ISBN: 978-3-030-45778-5

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-45778-5_1

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner