A4 Article in conference proceedings
State of the Art Literature Review on Network Anomaly Detection with Deep Learning (2018)


Bodström, T., & Hämäläinen, T. (2018). State of the Art Literature Review on Network Anomaly Detection with Deep Learning. In O. Galinina, S. Andreev, S. Balandin, & Y. Koucheryavy (Eds.), NEW2AN 2018, ruSMART 2018 : Internet of Things, Smart Spaces, and Next Generation Networks and Systems : 18th International Conference, NEW2AN 2018, and 11th Conference, ruSMART 2018, St. Petersburg, Russia, August 27–29, 2018, Proceedings (pp. 64-76). Springer. Lecture Notes in Computer Science, 11118. https://doi.org/10.1007/978-3-030-01168-0_7


JYU authors or editors


Publication details

All authors or editors: Bodström, Tero; Hämäläinen, Timo

Parent publication: NEW2AN 2018, ruSMART 2018 : Internet of Things, Smart Spaces, and Next Generation Networks and Systems : 18th International Conference, NEW2AN 2018, and 11th Conference, ruSMART 2018, St. Petersburg, Russia, August 27–29, 2018, Proceedings

Parent publication editors: Galinina, Olga; Andreev, Sergey; Balandin, Sergey; Koucheryavy, Yevgeni

ISBN: 978-3-030-01167-3

Journal or series: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Publication year: 2018

Number in series: 11118

Pages range: 64-76

Number of pages in the book: 705

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-030-01168-0_7

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/59976

Additional information: Also part of the Computer Communication Networks and Telecommunications book sub series (LNCCN, volume 11118).


Keywords: data security; cyber attacks; machine learning

Free keywords: network anomaly detection; deep learning


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2018

JUFO rating: 1


Last updated on 2023-10-01 at 12:32