Network security traffic detection and legal supervision based on adaptive metric learning algorithm
Network security mainly utilizes relevant equipment and programs of network systems to protect private information, in order to prevent damage, tampering, or leakage by illegal elements, and ensure the smooth operation of network system security. This paper proposes an adaptive algorithm of metric l...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025019486 |
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| _version_ | 1849434806989881344 |
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| author | Liu Zhen |
| author_facet | Liu Zhen |
| author_sort | Liu Zhen |
| collection | DOAJ |
| description | Network security mainly utilizes relevant equipment and programs of network systems to protect private information, in order to prevent damage, tampering, or leakage by illegal elements, and ensure the smooth operation of network system security. This paper proposes an adaptive algorithm of metric learning based on deep learning, which classifies and learns the source domain images, and adds and modifies the classification margin to enrich the aligned classification boundary images. After a series of experimental analysis, it was found that the vertex weighting algorithm proposed in this paper has better robustness and generalization compared to other algorithms, achieving better classification of target area images. Using the vertex weighted function algorithm, we can analyze the Time complexity of the original algorithm and calculate more accurate time values. Criminal law has a certain degree of lag, and its manifestation in cybersecurity crimes is relatively limited. It is necessary to break away from the original local limitations and take a comprehensive view. Overall, traditional criminal law theories and principles have high practical value and significance. |
| format | Article |
| id | doaj-art-3a04fa4595d146da915a1a8f7bb8c56d |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-3a04fa4595d146da915a1a8f7bb8c56d2025-08-20T03:26:30ZengElsevierResults in Engineering2590-12302025-09-012710587710.1016/j.rineng.2025.105877Network security traffic detection and legal supervision based on adaptive metric learning algorithmLiu Zhen0Wuhan Business University, Wuhan Hubei 430056, PR ChinaNetwork security mainly utilizes relevant equipment and programs of network systems to protect private information, in order to prevent damage, tampering, or leakage by illegal elements, and ensure the smooth operation of network system security. This paper proposes an adaptive algorithm of metric learning based on deep learning, which classifies and learns the source domain images, and adds and modifies the classification margin to enrich the aligned classification boundary images. After a series of experimental analysis, it was found that the vertex weighting algorithm proposed in this paper has better robustness and generalization compared to other algorithms, achieving better classification of target area images. Using the vertex weighted function algorithm, we can analyze the Time complexity of the original algorithm and calculate more accurate time values. Criminal law has a certain degree of lag, and its manifestation in cybersecurity crimes is relatively limited. It is necessary to break away from the original local limitations and take a comprehensive view. Overall, traditional criminal law theories and principles have high practical value and significance.http://www.sciencedirect.com/science/article/pii/S2590123025019486Deep learningVertex weightingNeural networkVertex coverage |
| spellingShingle | Liu Zhen Network security traffic detection and legal supervision based on adaptive metric learning algorithm Results in Engineering Deep learning Vertex weighting Neural network Vertex coverage |
| title | Network security traffic detection and legal supervision based on adaptive metric learning algorithm |
| title_full | Network security traffic detection and legal supervision based on adaptive metric learning algorithm |
| title_fullStr | Network security traffic detection and legal supervision based on adaptive metric learning algorithm |
| title_full_unstemmed | Network security traffic detection and legal supervision based on adaptive metric learning algorithm |
| title_short | Network security traffic detection and legal supervision based on adaptive metric learning algorithm |
| title_sort | network security traffic detection and legal supervision based on adaptive metric learning algorithm |
| topic | Deep learning Vertex weighting Neural network Vertex coverage |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025019486 |
| work_keys_str_mv | AT liuzhen networksecuritytrafficdetectionandlegalsupervisionbasedonadaptivemetriclearningalgorithm |