A novel anomaly detection method for multimodal WSN data flow via a dynamic graph neural network

Qinghao Zhanga School of Information and Communication, Guilin University of Electronic Technology, Guilin , People's Republic of China;b State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, People's Republic of ChinaView further author information

Miao Yea School of Information and Communication, Guilin University of Electronic Technology, Guilin , People's Republic of China;b State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, People's Republic of ChinaCorrespondence[email protected]
View further author information

Xiaofang Denga School of Information and Communication, Guilin University of Electronic Technology, Guilin , People's Republic of China;b State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, People's Republic of ChinaView further author information

Citations (1)

Keep up to date with the latest research on this topic with citation updates for this article.

A novel anomaly detection method for multimodal WSN data flow via a dynamic graph neural network

Articles from other publishers (1)

Information for

Open access

Opportunities

Help and information

Your download is now in progress and you may close this window

Login or register to access this feature

A novel anomaly detection method for multimodal WSN data flow via a dynamic graph neural network

Citations (1)

Articles from other publishers (1)

Related research

To cite this article:

Download citation

Information for

Open access

Opportunities

Help and information

Keep up to date