The Research on Dynamic Risk Assessment Based on Hidden Markov Models
In order to effectively finish the dynamic risk assessment of the electricity system, this paper will divide each attack into three distinct phases, The difficulty of attack is assessed by percent of each attack time of distinct stages take up in the total attack time to describe the attack difficul...
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| creator | Xiaorong Cheng Yangdan Ni |
| description | In order to effectively finish the dynamic risk assessment of the electricity system, this paper will divide each attack into three distinct phases, The difficulty of attack is assessed by percent of each attack time of distinct stages take up in the total attack time to describe the attack difficulty in order to determine the status of the assets transition matrix, realising the dynamic nature of risk assessment. The real-time dynamic risk assessment methods based on Hidden Markov Model HMM has a strong adaptability and scalability, it can be effectively applied on the network, host, system, service level of risk assessment. This paper designs and implements the dynamic risk assessment examples power system, and then demonstrateds the dynamic assessment model. |
| doi_str_mv | 10.1109/CSSS.2012.280 |
| format | Conference Proceeding |
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The real-time dynamic risk assessment methods based on Hidden Markov Model HMM has a strong adaptability and scalability, it can be effectively applied on the network, host, system, service level of risk assessment. This paper designs and implements the dynamic risk assessment examples power system, and then demonstrateds the dynamic assessment model.</description><identifier>ISBN: 9781467307215</identifier><identifier>ISBN: 1467307211</identifier><identifier>EISBN: 9780769547190</identifier><identifier>EISBN: 0769547192</identifier><identifier>DOI: 10.1109/CSSS.2012.280</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>data integration ; dynamic risk assessment ; Heuristic algorithms ; Hidden Markov ; Hidden Markov models ; Markov processes ; neural networks ; Power system dynamics ; Risk management ; Security ; Vectors</subject><ispartof>2012 International Conference on Computer Science and Service System, 2012, p.1106-1109</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6394518$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6394518$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Xiaorong Cheng</creatorcontrib><creatorcontrib>Yangdan Ni</creatorcontrib><title>The Research on Dynamic Risk Assessment Based on Hidden Markov Models</title><title>2012 International Conference on Computer Science and Service System</title><addtitle>csss</addtitle><description>In order to effectively finish the dynamic risk assessment of the electricity system, this paper will divide each attack into three distinct phases, The difficulty of attack is assessed by percent of each attack time of distinct stages take up in the total attack time to describe the attack difficulty in order to determine the status of the assets transition matrix, realising the dynamic nature of risk assessment. The real-time dynamic risk assessment methods based on Hidden Markov Model HMM has a strong adaptability and scalability, it can be effectively applied on the network, host, system, service level of risk assessment. This paper designs and implements the dynamic risk assessment examples power system, and then demonstrateds the dynamic assessment model.</description><subject>data integration</subject><subject>dynamic risk assessment</subject><subject>Heuristic algorithms</subject><subject>Hidden Markov</subject><subject>Hidden Markov models</subject><subject>Markov processes</subject><subject>neural networks</subject><subject>Power system dynamics</subject><subject>Risk management</subject><subject>Security</subject><subject>Vectors</subject><isbn>9781467307215</isbn><isbn>1467307211</isbn><isbn>9780769547190</isbn><isbn>0769547192</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjktLw0AUhUdEUGqWrtzMH0i888g8ljXWVmgRmuzLTOaGjm0SyRSh_976WB0OfOfjEPLAoGAM7FNV13XBgfGCG7gimdUGtLKl1MzC9W9nUmkBmrPylmQpfQDAZaq04Xdk0eyRbjGhm9o9HQf6ch5cH1u6jelA5ylhSj0OJ_rsEoYfYBVDwIFu3HQYv-hmDHhM9-Smc8eE2X_OSPO6aKpVvn5fvlXzdR4tnHIMreCcGe-dBd6pwJW-PPdCSM89EyEIx1F2zKsQWimcdKVQiNJ0gmsHYkYe_7QREXefU-zddN4pYWXJjPgGZMpLAw</recordid><startdate>201208</startdate><enddate>201208</enddate><creator>Xiaorong Cheng</creator><creator>Yangdan Ni</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201208</creationdate><title>The Research on Dynamic Risk Assessment Based on Hidden Markov Models</title><author>Xiaorong Cheng ; Yangdan Ni</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-edc32218bba902f6d267201b334b2b13dd3a2e4f1b6ddc43a4a536ee48f327a03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>data integration</topic><topic>dynamic risk assessment</topic><topic>Heuristic algorithms</topic><topic>Hidden Markov</topic><topic>Hidden Markov models</topic><topic>Markov processes</topic><topic>neural networks</topic><topic>Power system dynamics</topic><topic>Risk management</topic><topic>Security</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Xiaorong Cheng</creatorcontrib><creatorcontrib>Yangdan Ni</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xiaorong Cheng</au><au>Yangdan Ni</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The Research on Dynamic Risk Assessment Based on Hidden Markov Models</atitle><btitle>2012 International Conference on Computer Science and Service System</btitle><stitle>csss</stitle><date>2012-08</date><risdate>2012</risdate><spage>1106</spage><epage>1109</epage><pages>1106-1109</pages><isbn>9781467307215</isbn><isbn>1467307211</isbn><eisbn>9780769547190</eisbn><eisbn>0769547192</eisbn><coden>IEEPAD</coden><abstract>In order to effectively finish the dynamic risk assessment of the electricity system, this paper will divide each attack into three distinct phases, The difficulty of attack is assessed by percent of each attack time of distinct stages take up in the total attack time to describe the attack difficulty in order to determine the status of the assets transition matrix, realising the dynamic nature of risk assessment. The real-time dynamic risk assessment methods based on Hidden Markov Model HMM has a strong adaptability and scalability, it can be effectively applied on the network, host, system, service level of risk assessment. This paper designs and implements the dynamic risk assessment examples power system, and then demonstrateds the dynamic assessment model.</abstract><pub>IEEE</pub><doi>10.1109/CSSS.2012.280</doi><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | data integration dynamic risk assessment Heuristic algorithms Hidden Markov Hidden Markov models Markov processes neural networks Power system dynamics Risk management Security Vectors |
| title | The Research on Dynamic Risk Assessment Based on Hidden Markov Models |
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