Comparison of training parameters of DPA contest v4 dataset.
<p>Comparison of training parameters of DPA contest v4 dataset.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
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| _version_ | 1852021407207129088 |
|---|---|
| author | Hai Huang (140371) |
| author2 | Jinming Wu (7432031) Xinling Tang (2247616) Shilei Zhao (16859704) Zhiwei Liu (151279) Bin Yu (14464) |
| author2_role | author author author author author |
| author_facet | Hai Huang (140371) Jinming Wu (7432031) Xinling Tang (2247616) Shilei Zhao (16859704) Zhiwei Liu (151279) Bin Yu (14464) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hai Huang (140371) Jinming Wu (7432031) Xinling Tang (2247616) Shilei Zhao (16859704) Zhiwei Liu (151279) Bin Yu (14464) |
| dc.date.none.fl_str_mv | 2025-04-09T17:47:55Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0315340.t003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Comparison_of_training_parameters_of_DPA_contest_v4_dataset_/28764644 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified utilizes lstm layers low training efficiency demonstrated superior efficiency achieves faster convergence stacking network layers based network structure channel attack performance based improved side channel attacks compared network captures channel datasets channel attacks traditional methods thereby achi temporal coherence spatial details skip connections results indicate parallel processing paper proposes keys requires input data increased algorithmic experimental evaluations deep learning computational complexity comparative studies ascad dataset 30 traces 1 trace |
| dc.title.none.fl_str_mv | Comparison of training parameters of DPA contest v4 dataset. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Comparison of training parameters of DPA contest v4 dataset.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_95031d6df29ff25c4fafa43ee910df2a |
| identifier_str_mv | 10.1371/journal.pone.0315340.t003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28764644 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison of training parameters of DPA contest v4 dataset.Hai Huang (140371)Jinming Wu (7432031)Xinling Tang (2247616)Shilei Zhao (16859704)Zhiwei Liu (151279)Bin Yu (14464)Science PolicySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedutilizes lstm layerslow training efficiencydemonstrated superior efficiencyachieves faster convergencestacking network layersbased network structurechannel attack performancebased improved sidechannel attacks comparednetwork captureschannel datasetschannel attackstraditional methodsthereby achitemporal coherencespatial detailsskip connectionsresults indicateparallel processingpaper proposeskeys requiresinput dataincreased algorithmicexperimental evaluationsdeep learningcomputational complexitycomparative studiesascad dataset30 traces1 trace<p>Comparison of training parameters of DPA contest v4 dataset.</p>2025-04-09T17:47:55ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0315340.t003https://figshare.com/articles/dataset/Comparison_of_training_parameters_of_DPA_contest_v4_dataset_/28764644CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/287646442025-04-09T17:47:55Z |
| spellingShingle | Comparison of training parameters of DPA contest v4 dataset. Hai Huang (140371) Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified utilizes lstm layers low training efficiency demonstrated superior efficiency achieves faster convergence stacking network layers based network structure channel attack performance based improved side channel attacks compared network captures channel datasets channel attacks traditional methods thereby achi temporal coherence spatial details skip connections results indicate parallel processing paper proposes keys requires input data increased algorithmic experimental evaluations deep learning computational complexity comparative studies ascad dataset 30 traces 1 trace |
| status_str | publishedVersion |
| title | Comparison of training parameters of DPA contest v4 dataset. |
| title_full | Comparison of training parameters of DPA contest v4 dataset. |
| title_fullStr | Comparison of training parameters of DPA contest v4 dataset. |
| title_full_unstemmed | Comparison of training parameters of DPA contest v4 dataset. |
| title_short | Comparison of training parameters of DPA contest v4 dataset. |
| title_sort | Comparison of training parameters of DPA contest v4 dataset. |
| topic | Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified utilizes lstm layers low training efficiency demonstrated superior efficiency achieves faster convergence stacking network layers based network structure channel attack performance based improved side channel attacks compared network captures channel datasets channel attacks traditional methods thereby achi temporal coherence spatial details skip connections results indicate parallel processing paper proposes keys requires input data increased algorithmic experimental evaluations deep learning computational complexity comparative studies ascad dataset 30 traces 1 trace |