An Efficient Intrusion Detection Method Based on Dynamic Autoencoder

The proliferation of wireless sensor networks (WSNs) and their applications has attracted remarkable growth in unsolicited intrusions and security threats, which disrupt the normal operations of the WSNs. Deep learning (DL)-based network intrusion detection (NID) methods have been widely investigate...

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Bibliographic Details
Published in:	IEEE Wireless Communications Letters Vol. 10; no. 8; pp. 1707 - 1711
Main Authors:	Zhao, Ruijie, Yin, Jie, Xue, Zhi, Gui, Guan, Adebisi, Bamidele, Ohtsuki, Tomoaki, Gacanin, Haris, Sari, Hikmet
Format:	Journal Article
Language:	English Japanese
Published:	Piscataway IEEE 01.08.2021 Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks autoencoder Computational modeling Computing costs Deep learning Feature extraction intrusion detection Intrusion detection systems Lightweight lightweight neural network Machine learning Model accuracy Performance evaluation Robustness (mathematics) Training Wireless networks Wireless sensor networks
ISSN:	2162-2337, 2162-2345
Online Access:	Get full text
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