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Conference Paper: A Temporal Recurrent Neural Network for Detecting Abnormal Stock Movement: The Case of U.S. Financial Cybersecurity

TitleA Temporal Recurrent Neural Network for Detecting Abnormal Stock Movement: The Case of U.S. Financial Cybersecurity
Authors
Huang, YChung, WYTang, X
Issue Date2018
PublisherUniversity of South Florida.
Citation
Research Symposium of The Florida Center for Cybersecurity, Tampa, FL, USA, 3-4 April 2018 How to Cite?
Persistent Identifierhttp://hdl.handle.net/10722/278672

 

DC FieldValueLanguage
dc.contributor.authorHuang, Y-
dc.contributor.authorChung, WY-
dc.contributor.authorTang, X-
dc.date.accessioned2019-10-21T02:11:53Z-
dc.date.available2019-10-21T02:11:53Z-
dc.date.issued2018-
dc.identifier.citationResearch Symposium of The Florida Center for Cybersecurity, Tampa, FL, USA, 3-4 April 2018-
dc.identifier.urihttp://hdl.handle.net/10722/278672-
dc.languageeng-
dc.publisherUniversity of South Florida. -
dc.relation.ispartofResearch Symposium of The Florida Center for Cybersecurity-
dc.titleA Temporal Recurrent Neural Network for Detecting Abnormal Stock Movement: The Case of U.S. Financial Cybersecurity-
dc.typeConference_Paper-
dc.identifier.emailChung, WY: wchun@hku.hk-
dc.identifier.hkuros307661-
dc.publisher.placeTampa, FL-

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