%0 Conference Proceedings
%T DAFL: Deep Adaptive Feature Learning for Network Anomaly Detection
%+ Shenzhen Institutes of Advanced Technology (SIAT)
%+ University of Chinese Academy of Sciences [Beijing] (UCAS)
%+ Khoury College of Computer Sciences [Boston]
%+ Faculty of Science and Technology [Macau]
%A Ji, Shujian
%A Sun, Tongzheng
%A Ye, Kejiang
%A Wang, Wenbo
%A Xu, Cheng-Zhong
%Z Part 8: Short Papers
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 16th IFIP International Conference on Network and Parallel Computing (NPC)
%C Hohhot, China
%Y Xiaoxin Tang
%Y Quan Chen
%Y Pradip Bose
%Y Weiming Zheng
%Y Jean-Luc Gaudiot
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-11783
%P 350-354
%8 2019-08-23
%D 2019
%R 10.1007/978-3-030-30709-7_32
%K Network anomaly detection
%K Deep learning
%K Feature learning
%Z Computer Science [cs]Conference papers
%X With the rapid development of the Internet and the growing complexity of the network topology, network anomaly has become more diverse. In this paper, we propose an algorithm named Deep Adaptive Feature Learning (DAFL) for traffic anomaly detection based on deep learning model. By setting proper feature parameters $$\theta $$ on the neural network structure, DAFL can effectively generate low-dimensional new abstract features. Experimental results show the DAFL algorithm has good adaptability and robustness, which can effectively improve the detection accuracy and significantly reduce the detection time.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-03770566/document
%2 https://inria.hal.science/hal-03770566/file/486810_1_En_32_Chapter.pdf
%L hal-03770566
%U https://inria.hal.science/hal-03770566
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-11783