Detection Method of DC Microgrid Network Attack Based on Two-level and Multi-segment Model
Because the current method of attack detection of current flow microgrid is slow, it leads to the increase of detection time. Therefore, a two-stage segmentation model based attack detection method for DC microgrid network is proposed. The structure of DC microgrid is analyzed, the model of DC micro...
Gespeichert in:
| Veröffentlicht in: | Wireless personal communications 2022-11, Vol.127 (2), p.1665-1681 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1681 |
|---|---|
| container_issue | 2 |
| container_start_page | 1665 |
| container_title | Wireless personal communications |
| container_volume | 127 |
| creator | Zou, Liren |
| description | Because the current method of attack detection of current flow microgrid is slow, it leads to the increase of detection time. Therefore, a two-stage segmentation model based attack detection method for DC microgrid network is proposed. The structure of DC microgrid is analyzed, the model of DC microgrid network is constructed, and the sparse region of the micro grid network is divided. According to the results of regional division, a two-level and multi-segment model is constructed, which is composed of the above-level central neural network and the lower level is edge end neural network. The parallel computing structure of the model is set up to get the attack detection results of DC microgrid network. The experimental results show that the method has high recall and precision, low error detection rate and short detection time, which can realize the rapid and accurate detection of network attacks. |
| doi_str_mv | 10.1007/s11277-021-08711-w |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2740173851</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2740173851</sourcerecordid><originalsourceid>FETCH-LOGICAL-c249t-a9b9d4eddeab9201db2dece2940b0be90da38e00e69d2a3881ab3217789761003</originalsourceid><addsrcrecordid>eNp9kE1PAjEQhhujiYj-AU9NPFen3YVujwh-JaAXTIyXprsdcGHZYlvc-O-tYuLN08zhed7JvIScc7jkAPIqcC6kZCA4g0JyzroD0uMDKViR5S-HpAdKKDYUXByTkxBWAElTokdeJxixirVr6Qzjm7PULehkTGd15d3S15Y-YuycX9NRjKZa02sTMEEtnXeONfiBDTWtpbNdE2sWcLnBNtKZs9ickqOFaQKe_c4-eb69mY_v2fTp7mE8mrJK5Coyo0plc7QWTakEcFsKixUKlUMJJSqwJisQAIfKirQW3JSZ4FIWSg7TF1mfXOxzt9697zBEvXI736aTWsgcuMyKAU-U2FPprxA8LvTW1xvjPzUH_d2h3neoU4f6p0PdJSnbSyHB7RL9X_Q_1hfFrHRr</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2740173851</pqid></control><display><type>article</type><title>Detection Method of DC Microgrid Network Attack Based on Two-level and Multi-segment Model</title><source>SpringerLink Journals</source><creator>Zou, Liren</creator><creatorcontrib>Zou, Liren</creatorcontrib><description>Because the current method of attack detection of current flow microgrid is slow, it leads to the increase of detection time. Therefore, a two-stage segmentation model based attack detection method for DC microgrid network is proposed. The structure of DC microgrid is analyzed, the model of DC microgrid network is constructed, and the sparse region of the micro grid network is divided. According to the results of regional division, a two-level and multi-segment model is constructed, which is composed of the above-level central neural network and the lower level is edge end neural network. The parallel computing structure of the model is set up to get the attack detection results of DC microgrid network. The experimental results show that the method has high recall and precision, low error detection rate and short detection time, which can realize the rapid and accurate detection of network attacks.</description><identifier>ISSN: 0929-6212</identifier><identifier>EISSN: 1572-834X</identifier><identifier>DOI: 10.1007/s11277-021-08711-w</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Communications Engineering ; Computer Communication Networks ; Distributed generation ; Engineering ; Error detection ; Networks ; Neural networks ; Segments ; Signal,Image and Speech Processing</subject><ispartof>Wireless personal communications, 2022-11, Vol.127 (2), p.1665-1681</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c249t-a9b9d4eddeab9201db2dece2940b0be90da38e00e69d2a3881ab3217789761003</citedby><cites>FETCH-LOGICAL-c249t-a9b9d4eddeab9201db2dece2940b0be90da38e00e69d2a3881ab3217789761003</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11277-021-08711-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11277-021-08711-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Zou, Liren</creatorcontrib><title>Detection Method of DC Microgrid Network Attack Based on Two-level and Multi-segment Model</title><title>Wireless personal communications</title><addtitle>Wireless Pers Commun</addtitle><description>Because the current method of attack detection of current flow microgrid is slow, it leads to the increase of detection time. Therefore, a two-stage segmentation model based attack detection method for DC microgrid network is proposed. The structure of DC microgrid is analyzed, the model of DC microgrid network is constructed, and the sparse region of the micro grid network is divided. According to the results of regional division, a two-level and multi-segment model is constructed, which is composed of the above-level central neural network and the lower level is edge end neural network. The parallel computing structure of the model is set up to get the attack detection results of DC microgrid network. The experimental results show that the method has high recall and precision, low error detection rate and short detection time, which can realize the rapid and accurate detection of network attacks.</description><subject>Communications Engineering</subject><subject>Computer Communication Networks</subject><subject>Distributed generation</subject><subject>Engineering</subject><subject>Error detection</subject><subject>Networks</subject><subject>Neural networks</subject><subject>Segments</subject><subject>Signal,Image and Speech Processing</subject><issn>0929-6212</issn><issn>1572-834X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kE1PAjEQhhujiYj-AU9NPFen3YVujwh-JaAXTIyXprsdcGHZYlvc-O-tYuLN08zhed7JvIScc7jkAPIqcC6kZCA4g0JyzroD0uMDKViR5S-HpAdKKDYUXByTkxBWAElTokdeJxixirVr6Qzjm7PULehkTGd15d3S15Y-YuycX9NRjKZa02sTMEEtnXeONfiBDTWtpbNdE2sWcLnBNtKZs9ickqOFaQKe_c4-eb69mY_v2fTp7mE8mrJK5Coyo0plc7QWTakEcFsKixUKlUMJJSqwJisQAIfKirQW3JSZ4FIWSg7TF1mfXOxzt9697zBEvXI736aTWsgcuMyKAU-U2FPprxA8LvTW1xvjPzUH_d2h3neoU4f6p0PdJSnbSyHB7RL9X_Q_1hfFrHRr</recordid><startdate>20221101</startdate><enddate>20221101</enddate><creator>Zou, Liren</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20221101</creationdate><title>Detection Method of DC Microgrid Network Attack Based on Two-level and Multi-segment Model</title><author>Zou, Liren</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c249t-a9b9d4eddeab9201db2dece2940b0be90da38e00e69d2a3881ab3217789761003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Communications Engineering</topic><topic>Computer Communication Networks</topic><topic>Distributed generation</topic><topic>Engineering</topic><topic>Error detection</topic><topic>Networks</topic><topic>Neural networks</topic><topic>Segments</topic><topic>Signal,Image and Speech Processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zou, Liren</creatorcontrib><collection>CrossRef</collection><jtitle>Wireless personal communications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zou, Liren</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection Method of DC Microgrid Network Attack Based on Two-level and Multi-segment Model</atitle><jtitle>Wireless personal communications</jtitle><stitle>Wireless Pers Commun</stitle><date>2022-11-01</date><risdate>2022</risdate><volume>127</volume><issue>2</issue><spage>1665</spage><epage>1681</epage><pages>1665-1681</pages><issn>0929-6212</issn><eissn>1572-834X</eissn><abstract>Because the current method of attack detection of current flow microgrid is slow, it leads to the increase of detection time. Therefore, a two-stage segmentation model based attack detection method for DC microgrid network is proposed. The structure of DC microgrid is analyzed, the model of DC microgrid network is constructed, and the sparse region of the micro grid network is divided. According to the results of regional division, a two-level and multi-segment model is constructed, which is composed of the above-level central neural network and the lower level is edge end neural network. The parallel computing structure of the model is set up to get the attack detection results of DC microgrid network. The experimental results show that the method has high recall and precision, low error detection rate and short detection time, which can realize the rapid and accurate detection of network attacks.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11277-021-08711-w</doi><tpages>17</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0929-6212 |
| ispartof | Wireless personal communications, 2022-11, Vol.127 (2), p.1665-1681 |
| issn | 0929-6212 1572-834X |
| language | eng |
| recordid | cdi_proquest_journals_2740173851 |
| source | SpringerLink Journals |
| subjects | Communications Engineering Computer Communication Networks Distributed generation Engineering Error detection Networks Neural networks Segments Signal,Image and Speech Processing |
| title | Detection Method of DC Microgrid Network Attack Based on Two-level and Multi-segment Model |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T07%3A40%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Detection%20Method%20of%20DC%20Microgrid%20Network%20Attack%20Based%20on%20Two-level%20and%20Multi-segment%20Model&rft.jtitle=Wireless%20personal%20communications&rft.au=Zou,%20Liren&rft.date=2022-11-01&rft.volume=127&rft.issue=2&rft.spage=1665&rft.epage=1681&rft.pages=1665-1681&rft.issn=0929-6212&rft.eissn=1572-834X&rft_id=info:doi/10.1007/s11277-021-08711-w&rft_dat=%3Cproquest_cross%3E2740173851%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2740173851&rft_id=info:pmid/&rfr_iscdi=true |