A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data
There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced alloc...
Gespeichert in:
| Veröffentlicht in: | Computers, materials & continua materials & continua, 2018, Vol.56 (1), p.61 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 1 |
| container_start_page | 61 |
| container_title | Computers, materials & continua |
| container_volume | 56 |
| creator | Wu, Xiaonian Zhang, Chuyun Zhang, Runlian Wang, Yujue Cui, Jinhua |
| description | There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as good load balance, higher detection rate and detection efficiency. |
| doi_str_mv | 10.3970/cmc.2018.02449 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2396011251</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2396011251</sourcerecordid><originalsourceid>FETCH-LOGICAL-p211t-743792ada653fab70202a69789e9cf2c9bf4dab17718e483ebe6f05f624f9abe3</originalsourceid><addsrcrecordid>eNp9jjtPwzAUhS0EEqWwMltiTvHb8Zi2PCqVxwBzdePYlatgl8Tp7ycFxMh0j3S_80DompIZN5rc2g87Y4SWM8KEMCdoQqVQBWNMnf5pWp6ji77fEcIVN2SC2govQ5-7UA_ZNXgVczf0IUW8dNnZfFRPqXEtPgTAzyk2boSH8XFw-BW6HI5IiFsMscFzaCHaMaZq22Th2-1Th-dhi5eQ4RKdeWh7d_V7p-j9_u5t8VisXx5Wi2pd7BmludCCa8OgASW5h1oTRhgoo0vjjPXMmtqLBmqqNS2dKLmrnfJEesWEN1A7PkU3P7n7Ln0O4-LNLg1dHCs3jBtFKGWS_ktRSoiUkhj-BQq2Z0g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2396011251</pqid></control><display><type>article</type><title>A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data</title><source>EZB Electronic Journals Library</source><creator>Wu, Xiaonian ; Zhang, Chuyun ; Zhang, Runlian ; Wang, Yujue ; Cui, Jinhua</creator><creatorcontrib>Wu, Xiaonian ; Zhang, Chuyun ; Zhang, Runlian ; Wang, Yujue ; Cui, Jinhua</creatorcontrib><description>There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as good load balance, higher detection rate and detection efficiency.</description><identifier>ISSN: 1546-2218</identifier><identifier>EISSN: 1546-2226</identifier><identifier>DOI: 10.3970/cmc.2018.02449</identifier><language>eng</language><publisher>Henderson: Tech Science Press</publisher><subject>Big Data ; Computer simulation ; Cybersecurity ; Data integrity ; Data management ; Dynamic loads ; Integrity ; Intrusion detection systems ; Load balancing ; Network switching ; Partitioning ; Workload</subject><ispartof>Computers, materials & continua, 2018, Vol.56 (1), p.61</ispartof><rights>Copyright Tech Science Press 2018</rights><rights>2018. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4022,27922,27923,27924</link.rule.ids></links><search><creatorcontrib>Wu, Xiaonian</creatorcontrib><creatorcontrib>Zhang, Chuyun</creatorcontrib><creatorcontrib>Zhang, Runlian</creatorcontrib><creatorcontrib>Wang, Yujue</creatorcontrib><creatorcontrib>Cui, Jinhua</creatorcontrib><title>A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data</title><title>Computers, materials & continua</title><description>There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as good load balance, higher detection rate and detection efficiency.</description><subject>Big Data</subject><subject>Computer simulation</subject><subject>Cybersecurity</subject><subject>Data integrity</subject><subject>Data management</subject><subject>Dynamic loads</subject><subject>Integrity</subject><subject>Intrusion detection systems</subject><subject>Load balancing</subject><subject>Network switching</subject><subject>Partitioning</subject><subject>Workload</subject><issn>1546-2218</issn><issn>1546-2226</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9jjtPwzAUhS0EEqWwMltiTvHb8Zi2PCqVxwBzdePYlatgl8Tp7ycFxMh0j3S_80DompIZN5rc2g87Y4SWM8KEMCdoQqVQBWNMnf5pWp6ji77fEcIVN2SC2govQ5-7UA_ZNXgVczf0IUW8dNnZfFRPqXEtPgTAzyk2boSH8XFw-BW6HI5IiFsMscFzaCHaMaZq22Th2-1Th-dhi5eQ4RKdeWh7d_V7p-j9_u5t8VisXx5Wi2pd7BmludCCa8OgASW5h1oTRhgoo0vjjPXMmtqLBmqqNS2dKLmrnfJEesWEN1A7PkU3P7n7Ln0O4-LNLg1dHCs3jBtFKGWS_ktRSoiUkhj-BQq2Z0g</recordid><startdate>2018</startdate><enddate>2018</enddate><creator>Wu, Xiaonian</creator><creator>Zhang, Chuyun</creator><creator>Zhang, Runlian</creator><creator>Wang, Yujue</creator><creator>Cui, Jinhua</creator><general>Tech Science Press</general><scope>7SC</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>2018</creationdate><title>A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data</title><author>Wu, Xiaonian ; Zhang, Chuyun ; Zhang, Runlian ; Wang, Yujue ; Cui, Jinhua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p211t-743792ada653fab70202a69789e9cf2c9bf4dab17718e483ebe6f05f624f9abe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Big Data</topic><topic>Computer simulation</topic><topic>Cybersecurity</topic><topic>Data integrity</topic><topic>Data management</topic><topic>Dynamic loads</topic><topic>Integrity</topic><topic>Intrusion detection systems</topic><topic>Load balancing</topic><topic>Network switching</topic><topic>Partitioning</topic><topic>Workload</topic><toplevel>online_resources</toplevel><creatorcontrib>Wu, Xiaonian</creatorcontrib><creatorcontrib>Zhang, Chuyun</creatorcontrib><creatorcontrib>Zhang, Runlian</creatorcontrib><creatorcontrib>Wang, Yujue</creatorcontrib><creatorcontrib>Cui, Jinhua</creatorcontrib><collection>Computer and Information Systems Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Computers, materials & continua</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Xiaonian</au><au>Zhang, Chuyun</au><au>Zhang, Runlian</au><au>Wang, Yujue</au><au>Cui, Jinhua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data</atitle><jtitle>Computers, materials & continua</jtitle><date>2018</date><risdate>2018</risdate><volume>56</volume><issue>1</issue><spage>61</spage><pages>61-</pages><issn>1546-2218</issn><eissn>1546-2226</eissn><abstract>There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as good load balance, higher detection rate and detection efficiency.</abstract><cop>Henderson</cop><pub>Tech Science Press</pub><doi>10.3970/cmc.2018.02449</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1546-2218 |
| ispartof | Computers, materials & continua, 2018, Vol.56 (1), p.61 |
| issn | 1546-2218 1546-2226 |
| language | eng |
| recordid | cdi_proquest_journals_2396011251 |
| source | EZB Electronic Journals Library |
| subjects | Big Data Computer simulation Cybersecurity Data integrity Data management Dynamic loads Integrity Intrusion detection systems Load balancing Network switching Partitioning Workload |
| title | A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-10T19%3A16%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Distributed%20Intrusion%20Detection%20Model%20via%20Nondestructive%20Partitioning%20and%20Balanced%20Allocation%20for%20Big%20Data&rft.jtitle=Computers,%20materials%20&%20continua&rft.au=Wu,%20Xiaonian&rft.date=2018&rft.volume=56&rft.issue=1&rft.spage=61&rft.pages=61-&rft.issn=1546-2218&rft.eissn=1546-2226&rft_id=info:doi/10.3970/cmc.2018.02449&rft_dat=%3Cproquest%3E2396011251%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2396011251&rft_id=info:pmid/&rfr_iscdi=true |