Research on performance optimizations for TCM-KNN network anomaly detection algorithm
Based on TCM-KNN（transductive confidence machine for K-nearest neighbors） algorithm,the filter-based feature selection and cluster-based instance selection methods were used towards optimizing it as a lightweight network anomaly detection scheme,which not only reduced its complex feature space,but a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
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/74651426/ |
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| _version_ | 1841537640555347968 |
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| author | LI Yang1 GUO Li1 LU Tian-bo3 TIAN Zhi-hong1 |
| author_facet | LI Yang1 GUO Li1 LU Tian-bo3 TIAN Zhi-hong1 |
| author_sort | LI Yang1 |
| collection | DOAJ |
| description | Based on TCM-KNN（transductive confidence machine for K-nearest neighbors） algorithm,the filter-based feature selection and cluster-based instance selection methods were used towards optimizing it as a lightweight network anomaly detection scheme,which not only reduced its complex feature space,but also acquired high quality instances for training.A series of experimental results demonstrate the two methods for optimizations are actually effective in greatly reducing the computational costs while ensuring high detection performances for TCM-KNN algorithm.Therefore,the two methods make TCM-KNN be a good scheme for a lightweight network anomaly detection in practice. |
| format | Article |
| id | doaj-art-ae6d5c3e024d433daadb8981f4342714 |
| 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-ae6d5c3e024d433daadb8981f43427142025-01-14T08:28:57ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2009-01-0130131974651426Research on performance optimizations for TCM-KNN network anomaly detection algorithmLI Yang1GUO Li1LU Tian-bo3TIAN Zhi-hong1Based on TCM-KNN（transductive confidence machine for K-nearest neighbors） algorithm,the filter-based feature selection and cluster-based instance selection methods were used towards optimizing it as a lightweight network anomaly detection scheme,which not only reduced its complex feature space,but also acquired high quality instances for training.A series of experimental results demonstrate the two methods for optimizations are actually effective in greatly reducing the computational costs while ensuring high detection performances for TCM-KNN algorithm.Therefore,the two methods make TCM-KNN be a good scheme for a lightweight network anomaly detection in practice.http://www.joconline.com.cn/zh/article/74651426/network securityanomaly detectionTCM-KNN algorithmfeature selectioninstance selection |
| spellingShingle | LI Yang1 GUO Li1 LU Tian-bo3 TIAN Zhi-hong1 Research on performance optimizations for TCM-KNN network anomaly detection algorithm Tongxin xuebao network security anomaly detection TCM-KNN algorithm feature selection instance selection |
| title | Research on performance optimizations for TCM-KNN network anomaly detection algorithm |
| title_full | Research on performance optimizations for TCM-KNN network anomaly detection algorithm |
| title_fullStr | Research on performance optimizations for TCM-KNN network anomaly detection algorithm |
| title_full_unstemmed | Research on performance optimizations for TCM-KNN network anomaly detection algorithm |
| title_short | Research on performance optimizations for TCM-KNN network anomaly detection algorithm |
| title_sort | research on performance optimizations for tcm knn network anomaly detection algorithm |
| topic | network security anomaly detection TCM-KNN algorithm feature selection instance selection |
| url | http://www.joconline.com.cn/zh/article/74651426/ |
| work_keys_str_mv | AT liyang1 researchonperformanceoptimizationsfortcmknnnetworkanomalydetectionalgorithm AT guoli1 researchonperformanceoptimizationsfortcmknnnetworkanomalydetectionalgorithm AT lutianbo3 researchonperformanceoptimizationsfortcmknnnetworkanomalydetectionalgorithm AT tianzhihong1 researchonperformanceoptimizationsfortcmknnnetworkanomalydetectionalgorithm |