Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm
The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/473504 |
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| _version_ | 1832554600588115968 |
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| author | Yong Zeng Dacheng Liu Zhou Lei |
| author_facet | Yong Zeng Dacheng Liu Zhou Lei |
| author_sort | Yong Zeng |
| collection | DOAJ |
| description | The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously. |
| format | Article |
| id | doaj-art-e0659807d37048989edaef1683c2b383 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-e0659807d37048989edaef1683c2b3832025-02-03T05:51:08ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/473504473504Historical Feature Pattern Extraction Based Network Attack Situation Sensing AlgorithmYong Zeng0Dacheng Liu1Zhou Lei2Department of Industrial Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Industrial Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Industrial Engineering, Tsinghua University, Beijing 100084, ChinaThe situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.http://dx.doi.org/10.1155/2014/473504 |
| spellingShingle | Yong Zeng Dacheng Liu Zhou Lei Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm The Scientific World Journal |
| title | Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm |
| title_full | Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm |
| title_fullStr | Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm |
| title_full_unstemmed | Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm |
| title_short | Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm |
| title_sort | historical feature pattern extraction based network attack situation sensing algorithm |
| url | http://dx.doi.org/10.1155/2014/473504 |
| work_keys_str_mv | AT yongzeng historicalfeaturepatternextractionbasednetworkattacksituationsensingalgorithm AT dachengliu historicalfeaturepatternextractionbasednetworkattacksituationsensingalgorithm AT zhoulei historicalfeaturepatternextractionbasednetworkattacksituationsensingalgorithm |