Anomaly detection algorithm based on fractal characteristics of large-scale network traffic
Based on the fractal structure of the large-scale network traffic aggregation, anomalies were analyzed qualitatively and quantitatively from perspective of the global and local scaling exponents.Multi-fractal singular spectrum and Lipschitz regularity distribution were used to analyze the fractal pa...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2009-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/74651281/ |
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| _version_ | 1841537589524299776 |
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| author | XU Xiao-dong1 ZHU Shi-rui2 SUN Ya-min1 |
| author_facet | XU Xiao-dong1 ZHU Shi-rui2 SUN Ya-min1 |
| author_sort | XU Xiao-dong1 |
| collection | DOAJ |
| description | Based on the fractal structure of the large-scale network traffic aggregation, anomalies were analyzed qualitatively and quantitatively from perspective of the global and local scaling exponents.Multi-fractal singular spectrum and Lipschitz regularity distribution were used to analyze the fractal parameters of abnormal flow, trying to identify the relationship between the changes of these parameters and the emergence of anomalies.Experimental results show that the emergence of anomalies has obvious signs on the singular spectrum and Lipschitz regularity distribution.Using this feature, a new multi-fractal-based anomaly detection algorithm and a new detection framework were constructed.On the DARPA/Lincoln laboratory intrusion detection evaluation data set 1999, this algorithm’s detection rate is high at low false alarm rate, which is better than EMERALD. |
| format | Article |
| id | doaj-art-0bcfa5e0eccd40b1a07651e7f9b8674e |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2009-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-0bcfa5e0eccd40b1a07651e7f9b8674e2025-01-14T08:28:27ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2009-01-0130435374651281Anomaly detection algorithm based on fractal characteristics of large-scale network trafficXU Xiao-dong1ZHU Shi-rui2SUN Ya-min1Based on the fractal structure of the large-scale network traffic aggregation, anomalies were analyzed qualitatively and quantitatively from perspective of the global and local scaling exponents.Multi-fractal singular spectrum and Lipschitz regularity distribution were used to analyze the fractal parameters of abnormal flow, trying to identify the relationship between the changes of these parameters and the emergence of anomalies.Experimental results show that the emergence of anomalies has obvious signs on the singular spectrum and Lipschitz regularity distribution.Using this feature, a new multi-fractal-based anomaly detection algorithm and a new detection framework were constructed.On the DARPA/Lincoln laboratory intrusion detection evaluation data set 1999, this algorithm’s detection rate is high at low false alarm rate, which is better than EMERALD.http://www.joconline.com.cn/zh/article/74651281/anomaly detectionmulti-fractal singularity spectrumLipschitz regularity distribution |
| spellingShingle | XU Xiao-dong1 ZHU Shi-rui2 SUN Ya-min1 Anomaly detection algorithm based on fractal characteristics of large-scale network traffic Tongxin xuebao anomaly detection multi-fractal singularity spectrum Lipschitz regularity distribution |
| title | Anomaly detection algorithm based on fractal characteristics of large-scale network traffic |
| title_full | Anomaly detection algorithm based on fractal characteristics of large-scale network traffic |
| title_fullStr | Anomaly detection algorithm based on fractal characteristics of large-scale network traffic |
| title_full_unstemmed | Anomaly detection algorithm based on fractal characteristics of large-scale network traffic |
| title_short | Anomaly detection algorithm based on fractal characteristics of large-scale network traffic |
| title_sort | anomaly detection algorithm based on fractal characteristics of large scale network traffic |
| topic | anomaly detection multi-fractal singularity spectrum Lipschitz regularity distribution |
| url | http://www.joconline.com.cn/zh/article/74651281/ |
| work_keys_str_mv | AT xuxiaodong1 anomalydetectionalgorithmbasedonfractalcharacteristicsoflargescalenetworktraffic AT zhushirui2 anomalydetectionalgorithmbasedonfractalcharacteristicsoflargescalenetworktraffic AT sunyamin1 anomalydetectionalgorithmbasedonfractalcharacteristicsoflargescalenetworktraffic |