GraphNEI: A GNN-based network entity identification method for IP geolocation
Network entity geolocation technology is a technique for inferring geographic location through features such as network measurements or IP address searchable information, also known as IP geolocation. By obtaining the device type of the target can assist in target IP geolocation or IP landmark minin...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ma, Zhaorui [verfasserIn] Zhang, Shicheng [verfasserIn] Li, Na [verfasserIn] Li, Tianao [verfasserIn] Hu, Xinhao [verfasserIn] Feng, Hao [verfasserIn] Zhou, Qinglei [verfasserIn] Liu, Fenlin [verfasserIn] Quan, Xiaowen [verfasserIn] Wang, Hongjian [verfasserIn] Hu, Guangwu [verfasserIn] Zhang, Shubo [verfasserIn] Zhai, Yaqi [verfasserIn] Chen, Shuaibin [verfasserIn] Zhang, Shuaiwei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Computer networks - Amsterdam [u.a.] : Elsevier, 1976, 235 |
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| Übergeordnetes Werk: |
volume:235 |
| DOI / URN: |
10.1016/j.comnet.2023.109946 |
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| Katalog-ID: |
ELV06430552X |
|---|
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| 520 | |a Network entity geolocation technology is a technique for inferring geographic location through features such as network measurements or IP address searchable information, also known as IP geolocation. By obtaining the device type of the target can assist in target IP geolocation or IP landmark mining. Most traditional rule-based or learning-based methods identify network entities. However, due to the existence of firewalls and the limitations of feature detection, the features of individual targets are prone to be missing or spoofed, which can lead to a decrease in the accuracy of network entity identification (NEI). This paper propose a graph neural network-based network entity identification method, GraphNEI model, which embeds network entities into the graph structure and uses graph neural networks to improve the current problem of missing or spoofed features in order to improve the accuracy of current network entity identification. It mainly includes five steps: data processing, subgraph partition, weight calculation, node update and classification. First, the acquired dataset is subjected to feature extraction and anonymization; second, the target nodes are subjected to network topology graph construction and community division; third, the nodes’ self-attention, structural attention and similarity of neighbors are combined and used to calculate the nodes’ combined attention; fourth, node aggregation and update, and update the node representation based on the combined attention results; finally, the nodes are classified. We successfully identified 7 different network entities on the publicly collected dataset, and the identification accuracy of network entities is above 95.49%, improved 0.51%–10.42% compared to typical rule-based or learning-based methods. It effectively improves the effective NEI in the absence of network entity features. In addition, we conduct IP geolocation research based on NEI, and the experimental results show that the method is effective in reducing IP geolocation errors distance. | ||
| 650 | 4 | |a Network entity identification | |
| 650 | 4 | |a Entity geolocation | |
| 650 | 4 | |a Graph neural networks | |
| 650 | 4 | |a Transformer | |
| 700 | 1 | |a Zhang, Shicheng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Na |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Tianao |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Xinhao |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Qinglei |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Fenlin |e verfasserin |4 aut | |
| 700 | 1 | |a Quan, Xiaowen |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Hongjian |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Guangwu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shubo |e verfasserin |4 aut | |
| 700 | 1 | |a Zhai, Yaqi |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Shuaibin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shuaiwei |e verfasserin |4 aut | |
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10.1016/j.comnet.2023.109946 doi (DE-627)ELV06430552X (ELSEVIER)S1389-1286(23)00391-2 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Ma, Zhaorui verfasserin aut GraphNEI: A GNN-based network entity identification method for IP geolocation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Network entity geolocation technology is a technique for inferring geographic location through features such as network measurements or IP address searchable information, also known as IP geolocation. By obtaining the device type of the target can assist in target IP geolocation or IP landmark mining. Most traditional rule-based or learning-based methods identify network entities. However, due to the existence of firewalls and the limitations of feature detection, the features of individual targets are prone to be missing or spoofed, which can lead to a decrease in the accuracy of network entity identification (NEI). This paper propose a graph neural network-based network entity identification method, GraphNEI model, which embeds network entities into the graph structure and uses graph neural networks to improve the current problem of missing or spoofed features in order to improve the accuracy of current network entity identification. It mainly includes five steps: data processing, subgraph partition, weight calculation, node update and classification. First, the acquired dataset is subjected to feature extraction and anonymization; second, the target nodes are subjected to network topology graph construction and community division; third, the nodes’ self-attention, structural attention and similarity of neighbors are combined and used to calculate the nodes’ combined attention; fourth, node aggregation and update, and update the node representation based on the combined attention results; finally, the nodes are classified. We successfully identified 7 different network entities on the publicly collected dataset, and the identification accuracy of network entities is above 95.49%, improved 0.51%–10.42% compared to typical rule-based or learning-based methods. It effectively improves the effective NEI in the absence of network entity features. In addition, we conduct IP geolocation research based on NEI, and the experimental results show that the method is effective in reducing IP geolocation errors distance. Network entity identification Entity geolocation Graph neural networks Transformer Zhang, Shicheng verfasserin aut Li, Na verfasserin aut Li, Tianao verfasserin aut Hu, Xinhao verfasserin aut Feng, Hao verfasserin aut Zhou, Qinglei verfasserin aut Liu, Fenlin verfasserin aut Quan, Xiaowen verfasserin aut Wang, Hongjian verfasserin aut Hu, Guangwu verfasserin aut Zhang, Shubo verfasserin aut Zhai, Yaqi verfasserin aut Chen, Shuaibin verfasserin aut Zhang, Shuaiwei verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 235 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:235 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 235 |
| spelling |
10.1016/j.comnet.2023.109946 doi (DE-627)ELV06430552X (ELSEVIER)S1389-1286(23)00391-2 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Ma, Zhaorui verfasserin aut GraphNEI: A GNN-based network entity identification method for IP geolocation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Network entity geolocation technology is a technique for inferring geographic location through features such as network measurements or IP address searchable information, also known as IP geolocation. By obtaining the device type of the target can assist in target IP geolocation or IP landmark mining. Most traditional rule-based or learning-based methods identify network entities. However, due to the existence of firewalls and the limitations of feature detection, the features of individual targets are prone to be missing or spoofed, which can lead to a decrease in the accuracy of network entity identification (NEI). This paper propose a graph neural network-based network entity identification method, GraphNEI model, which embeds network entities into the graph structure and uses graph neural networks to improve the current problem of missing or spoofed features in order to improve the accuracy of current network entity identification. It mainly includes five steps: data processing, subgraph partition, weight calculation, node update and classification. First, the acquired dataset is subjected to feature extraction and anonymization; second, the target nodes are subjected to network topology graph construction and community division; third, the nodes’ self-attention, structural attention and similarity of neighbors are combined and used to calculate the nodes’ combined attention; fourth, node aggregation and update, and update the node representation based on the combined attention results; finally, the nodes are classified. We successfully identified 7 different network entities on the publicly collected dataset, and the identification accuracy of network entities is above 95.49%, improved 0.51%–10.42% compared to typical rule-based or learning-based methods. It effectively improves the effective NEI in the absence of network entity features. In addition, we conduct IP geolocation research based on NEI, and the experimental results show that the method is effective in reducing IP geolocation errors distance. Network entity identification Entity geolocation Graph neural networks Transformer Zhang, Shicheng verfasserin aut Li, Na verfasserin aut Li, Tianao verfasserin aut Hu, Xinhao verfasserin aut Feng, Hao verfasserin aut Zhou, Qinglei verfasserin aut Liu, Fenlin verfasserin aut Quan, Xiaowen verfasserin aut Wang, Hongjian verfasserin aut Hu, Guangwu verfasserin aut Zhang, Shubo verfasserin aut Zhai, Yaqi verfasserin aut Chen, Shuaibin verfasserin aut Zhang, Shuaiwei verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 235 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:235 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 235 |
| allfields_unstemmed |
10.1016/j.comnet.2023.109946 doi (DE-627)ELV06430552X (ELSEVIER)S1389-1286(23)00391-2 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Ma, Zhaorui verfasserin aut GraphNEI: A GNN-based network entity identification method for IP geolocation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Network entity geolocation technology is a technique for inferring geographic location through features such as network measurements or IP address searchable information, also known as IP geolocation. By obtaining the device type of the target can assist in target IP geolocation or IP landmark mining. Most traditional rule-based or learning-based methods identify network entities. However, due to the existence of firewalls and the limitations of feature detection, the features of individual targets are prone to be missing or spoofed, which can lead to a decrease in the accuracy of network entity identification (NEI). This paper propose a graph neural network-based network entity identification method, GraphNEI model, which embeds network entities into the graph structure and uses graph neural networks to improve the current problem of missing or spoofed features in order to improve the accuracy of current network entity identification. It mainly includes five steps: data processing, subgraph partition, weight calculation, node update and classification. First, the acquired dataset is subjected to feature extraction and anonymization; second, the target nodes are subjected to network topology graph construction and community division; third, the nodes’ self-attention, structural attention and similarity of neighbors are combined and used to calculate the nodes’ combined attention; fourth, node aggregation and update, and update the node representation based on the combined attention results; finally, the nodes are classified. We successfully identified 7 different network entities on the publicly collected dataset, and the identification accuracy of network entities is above 95.49%, improved 0.51%–10.42% compared to typical rule-based or learning-based methods. It effectively improves the effective NEI in the absence of network entity features. In addition, we conduct IP geolocation research based on NEI, and the experimental results show that the method is effective in reducing IP geolocation errors distance. Network entity identification Entity geolocation Graph neural networks Transformer Zhang, Shicheng verfasserin aut Li, Na verfasserin aut Li, Tianao verfasserin aut Hu, Xinhao verfasserin aut Feng, Hao verfasserin aut Zhou, Qinglei verfasserin aut Liu, Fenlin verfasserin aut Quan, Xiaowen verfasserin aut Wang, Hongjian verfasserin aut Hu, Guangwu verfasserin aut Zhang, Shubo verfasserin aut Zhai, Yaqi verfasserin aut Chen, Shuaibin verfasserin aut Zhang, Shuaiwei verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 235 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:235 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 235 |
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10.1016/j.comnet.2023.109946 doi (DE-627)ELV06430552X (ELSEVIER)S1389-1286(23)00391-2 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Ma, Zhaorui verfasserin aut GraphNEI: A GNN-based network entity identification method for IP geolocation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Network entity geolocation technology is a technique for inferring geographic location through features such as network measurements or IP address searchable information, also known as IP geolocation. By obtaining the device type of the target can assist in target IP geolocation or IP landmark mining. Most traditional rule-based or learning-based methods identify network entities. However, due to the existence of firewalls and the limitations of feature detection, the features of individual targets are prone to be missing or spoofed, which can lead to a decrease in the accuracy of network entity identification (NEI). This paper propose a graph neural network-based network entity identification method, GraphNEI model, which embeds network entities into the graph structure and uses graph neural networks to improve the current problem of missing or spoofed features in order to improve the accuracy of current network entity identification. It mainly includes five steps: data processing, subgraph partition, weight calculation, node update and classification. First, the acquired dataset is subjected to feature extraction and anonymization; second, the target nodes are subjected to network topology graph construction and community division; third, the nodes’ self-attention, structural attention and similarity of neighbors are combined and used to calculate the nodes’ combined attention; fourth, node aggregation and update, and update the node representation based on the combined attention results; finally, the nodes are classified. We successfully identified 7 different network entities on the publicly collected dataset, and the identification accuracy of network entities is above 95.49%, improved 0.51%–10.42% compared to typical rule-based or learning-based methods. It effectively improves the effective NEI in the absence of network entity features. In addition, we conduct IP geolocation research based on NEI, and the experimental results show that the method is effective in reducing IP geolocation errors distance. Network entity identification Entity geolocation Graph neural networks Transformer Zhang, Shicheng verfasserin aut Li, Na verfasserin aut Li, Tianao verfasserin aut Hu, Xinhao verfasserin aut Feng, Hao verfasserin aut Zhou, Qinglei verfasserin aut Liu, Fenlin verfasserin aut Quan, Xiaowen verfasserin aut Wang, Hongjian verfasserin aut Hu, Guangwu verfasserin aut Zhang, Shubo verfasserin aut Zhai, Yaqi verfasserin aut Chen, Shuaibin verfasserin aut Zhang, Shuaiwei verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 235 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:235 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 235 |
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10.1016/j.comnet.2023.109946 doi (DE-627)ELV06430552X (ELSEVIER)S1389-1286(23)00391-2 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Ma, Zhaorui verfasserin aut GraphNEI: A GNN-based network entity identification method for IP geolocation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Network entity geolocation technology is a technique for inferring geographic location through features such as network measurements or IP address searchable information, also known as IP geolocation. By obtaining the device type of the target can assist in target IP geolocation or IP landmark mining. Most traditional rule-based or learning-based methods identify network entities. However, due to the existence of firewalls and the limitations of feature detection, the features of individual targets are prone to be missing or spoofed, which can lead to a decrease in the accuracy of network entity identification (NEI). This paper propose a graph neural network-based network entity identification method, GraphNEI model, which embeds network entities into the graph structure and uses graph neural networks to improve the current problem of missing or spoofed features in order to improve the accuracy of current network entity identification. It mainly includes five steps: data processing, subgraph partition, weight calculation, node update and classification. First, the acquired dataset is subjected to feature extraction and anonymization; second, the target nodes are subjected to network topology graph construction and community division; third, the nodes’ self-attention, structural attention and similarity of neighbors are combined and used to calculate the nodes’ combined attention; fourth, node aggregation and update, and update the node representation based on the combined attention results; finally, the nodes are classified. We successfully identified 7 different network entities on the publicly collected dataset, and the identification accuracy of network entities is above 95.49%, improved 0.51%–10.42% compared to typical rule-based or learning-based methods. It effectively improves the effective NEI in the absence of network entity features. In addition, we conduct IP geolocation research based on NEI, and the experimental results show that the method is effective in reducing IP geolocation errors distance. Network entity identification Entity geolocation Graph neural networks Transformer Zhang, Shicheng verfasserin aut Li, Na verfasserin aut Li, Tianao verfasserin aut Hu, Xinhao verfasserin aut Feng, Hao verfasserin aut Zhou, Qinglei verfasserin aut Liu, Fenlin verfasserin aut Quan, Xiaowen verfasserin aut Wang, Hongjian verfasserin aut Hu, Guangwu verfasserin aut Zhang, Shubo verfasserin aut Zhai, Yaqi verfasserin aut Chen, Shuaibin verfasserin aut Zhang, Shuaiwei verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 235 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:235 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 235 |
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Ma, Zhaorui @@aut@@ Zhang, Shicheng @@aut@@ Li, Na @@aut@@ Li, Tianao @@aut@@ Hu, Xinhao @@aut@@ Feng, Hao @@aut@@ Zhou, Qinglei @@aut@@ Liu, Fenlin @@aut@@ Quan, Xiaowen @@aut@@ Wang, Hongjian @@aut@@ Hu, Guangwu @@aut@@ Zhang, Shubo @@aut@@ Zhai, Yaqi @@aut@@ Chen, Shuaibin @@aut@@ Zhang, Shuaiwei @@aut@@ |
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2023-01-01T00:00:00Z |
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Ma, Zhaorui |
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Ma, Zhaorui Zhang, Shicheng Li, Na Li, Tianao Hu, Xinhao Feng, Hao Zhou, Qinglei Liu, Fenlin Quan, Xiaowen Wang, Hongjian Hu, Guangwu Zhang, Shubo Zhai, Yaqi Chen, Shuaibin Zhang, Shuaiwei |
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10.1016/j.comnet.2023.109946 |
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graphnei: a gnn-based network entity identification method for ip geolocation |
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GraphNEI: A GNN-based network entity identification method for IP geolocation |
| abstract |
Network entity geolocation technology is a technique for inferring geographic location through features such as network measurements or IP address searchable information, also known as IP geolocation. By obtaining the device type of the target can assist in target IP geolocation or IP landmark mining. Most traditional rule-based or learning-based methods identify network entities. However, due to the existence of firewalls and the limitations of feature detection, the features of individual targets are prone to be missing or spoofed, which can lead to a decrease in the accuracy of network entity identification (NEI). This paper propose a graph neural network-based network entity identification method, GraphNEI model, which embeds network entities into the graph structure and uses graph neural networks to improve the current problem of missing or spoofed features in order to improve the accuracy of current network entity identification. It mainly includes five steps: data processing, subgraph partition, weight calculation, node update and classification. First, the acquired dataset is subjected to feature extraction and anonymization; second, the target nodes are subjected to network topology graph construction and community division; third, the nodes’ self-attention, structural attention and similarity of neighbors are combined and used to calculate the nodes’ combined attention; fourth, node aggregation and update, and update the node representation based on the combined attention results; finally, the nodes are classified. We successfully identified 7 different network entities on the publicly collected dataset, and the identification accuracy of network entities is above 95.49%, improved 0.51%–10.42% compared to typical rule-based or learning-based methods. It effectively improves the effective NEI in the absence of network entity features. In addition, we conduct IP geolocation research based on NEI, and the experimental results show that the method is effective in reducing IP geolocation errors distance. |
| abstractGer |
Network entity geolocation technology is a technique for inferring geographic location through features such as network measurements or IP address searchable information, also known as IP geolocation. By obtaining the device type of the target can assist in target IP geolocation or IP landmark mining. Most traditional rule-based or learning-based methods identify network entities. However, due to the existence of firewalls and the limitations of feature detection, the features of individual targets are prone to be missing or spoofed, which can lead to a decrease in the accuracy of network entity identification (NEI). This paper propose a graph neural network-based network entity identification method, GraphNEI model, which embeds network entities into the graph structure and uses graph neural networks to improve the current problem of missing or spoofed features in order to improve the accuracy of current network entity identification. It mainly includes five steps: data processing, subgraph partition, weight calculation, node update and classification. First, the acquired dataset is subjected to feature extraction and anonymization; second, the target nodes are subjected to network topology graph construction and community division; third, the nodes’ self-attention, structural attention and similarity of neighbors are combined and used to calculate the nodes’ combined attention; fourth, node aggregation and update, and update the node representation based on the combined attention results; finally, the nodes are classified. We successfully identified 7 different network entities on the publicly collected dataset, and the identification accuracy of network entities is above 95.49%, improved 0.51%–10.42% compared to typical rule-based or learning-based methods. It effectively improves the effective NEI in the absence of network entity features. In addition, we conduct IP geolocation research based on NEI, and the experimental results show that the method is effective in reducing IP geolocation errors distance. |
| abstract_unstemmed |
Network entity geolocation technology is a technique for inferring geographic location through features such as network measurements or IP address searchable information, also known as IP geolocation. By obtaining the device type of the target can assist in target IP geolocation or IP landmark mining. Most traditional rule-based or learning-based methods identify network entities. However, due to the existence of firewalls and the limitations of feature detection, the features of individual targets are prone to be missing or spoofed, which can lead to a decrease in the accuracy of network entity identification (NEI). This paper propose a graph neural network-based network entity identification method, GraphNEI model, which embeds network entities into the graph structure and uses graph neural networks to improve the current problem of missing or spoofed features in order to improve the accuracy of current network entity identification. It mainly includes five steps: data processing, subgraph partition, weight calculation, node update and classification. First, the acquired dataset is subjected to feature extraction and anonymization; second, the target nodes are subjected to network topology graph construction and community division; third, the nodes’ self-attention, structural attention and similarity of neighbors are combined and used to calculate the nodes’ combined attention; fourth, node aggregation and update, and update the node representation based on the combined attention results; finally, the nodes are classified. We successfully identified 7 different network entities on the publicly collected dataset, and the identification accuracy of network entities is above 95.49%, improved 0.51%–10.42% compared to typical rule-based or learning-based methods. It effectively improves the effective NEI in the absence of network entity features. In addition, we conduct IP geolocation research based on NEI, and the experimental results show that the method is effective in reducing IP geolocation errors distance. |
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GraphNEI: A GNN-based network entity identification method for IP geolocation |
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Zhang, Shicheng Li, Na Li, Tianao Hu, Xinhao Feng, Hao Zhou, Qinglei Liu, Fenlin Quan, Xiaowen Wang, Hongjian Hu, Guangwu Zhang, Shubo Zhai, Yaqi Chen, Shuaibin Zhang, Shuaiwei |
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| score |
7.400546 |