Self-similarity analysis of network threat time series
In order to validate the feasibility and applicability of the chaotic prediction model of network threat time se-ries,a fractal self-similarity analysis method for network threat time series based on the R/S （rescaled range） analysis was proposed. Using this method,the Hurst exponent of the represen...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2008-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/74656710/ |
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| _version_ | 1850123920412246016 |
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| author | XUAN Lei LU Xi-cheng YU Rui-hou ZHAO Xue-ming |
| author_facet | XUAN Lei LU Xi-cheng YU Rui-hou ZHAO Xue-ming |
| author_sort | XUAN Lei |
| collection | DOAJ |
| description | In order to validate the feasibility and applicability of the chaotic prediction model of network threat time se-ries,a fractal self-similarity analysis method for network threat time series based on the R/S （rescaled range） analysis was proposed. Using this method,the Hurst exponent of the representative samples from the three data sets of network threat were computed and tested. It was verified that there exist statistic self-similarities in continuous and non-sparse discrete time series of network threat so that it will be feasible to predict. On the other hand,there is no statistic self-similarity in sparse discrete threat time series and it will be very difficult to predict. The research outcome establishes the theory base to utilize the complex non-linearity system theory such as fractal and chaos to process the information security risk as-sessment and network threat prediction. |
| format | Article |
| id | doaj-art-d7e58a6efac44bf3bbb007369ebfecce |
| institution | OA Journals |
| issn | 1000-436X |
| language | zho |
| publishDate | 2008-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-d7e58a6efac44bf3bbb007369ebfecce2025-08-20T02:34:28ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2008-01-01455074656710Self-similarity analysis of network threat time seriesXUAN LeiLU Xi-chengYU Rui-houZHAO Xue-mingIn order to validate the feasibility and applicability of the chaotic prediction model of network threat time se-ries,a fractal self-similarity analysis method for network threat time series based on the R/S （rescaled range） analysis was proposed. Using this method,the Hurst exponent of the representative samples from the three data sets of network threat were computed and tested. It was verified that there exist statistic self-similarities in continuous and non-sparse discrete time series of network threat so that it will be feasible to predict. On the other hand,there is no statistic self-similarity in sparse discrete threat time series and it will be very difficult to predict. The research outcome establishes the theory base to utilize the complex non-linearity system theory such as fractal and chaos to process the information security risk as-sessment and network threat prediction.http://www.joconline.com.cn/zh/article/74656710/network threat time seriesfractalself-similarityHurst exponent |
| spellingShingle | XUAN Lei LU Xi-cheng YU Rui-hou ZHAO Xue-ming Self-similarity analysis of network threat time series Tongxin xuebao network threat time series fractal self-similarity Hurst exponent |
| title | Self-similarity analysis of network threat time series |
| title_full | Self-similarity analysis of network threat time series |
| title_fullStr | Self-similarity analysis of network threat time series |
| title_full_unstemmed | Self-similarity analysis of network threat time series |
| title_short | Self-similarity analysis of network threat time series |
| title_sort | self similarity analysis of network threat time series |
| topic | network threat time series fractal self-similarity Hurst exponent |
| url | http://www.joconline.com.cn/zh/article/74656710/ |
| work_keys_str_mv | AT xuanlei selfsimilarityanalysisofnetworkthreattimeseries AT luxicheng selfsimilarityanalysisofnetworkthreattimeseries AT yuruihou selfsimilarityanalysisofnetworkthreattimeseries AT zhaoxueming selfsimilarityanalysisofnetworkthreattimeseries |