Identifying VoIP traffic in VPN tunnel via Flow Spatio-Temporal Features
The persistent emergence of new network applications, along with encrypted network communication, has make traffic analysis become a challenging issue in network management and cyberspace security. Currently, virtual private network (VPNs) has become one of the most popular encrypted communication s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2020-07-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2020260?viewType=HTML |
| _version_ | 1818647676038676480 |
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| author | Faiz Ul Islam Guangjie Liu Weiwei Liu |
| author_facet | Faiz Ul Islam Guangjie Liu Weiwei Liu |
| author_sort | Faiz Ul Islam |
| collection | DOAJ |
| description | The persistent emergence of new network applications, along with encrypted network communication, has make traffic analysis become a challenging issue in network management and cyberspace security. Currently, virtual private network (VPNs) has become one of the most popular encrypted communication services for bypassing censorship and guarantee remote access to geographically locked services. In this paper, a novel identification scheme of VoIP traffic tunneled through VPN is proposed. We employed a set of Flow Spatio-Temporal Features (FSTF) to six well-known classifiers, including decision trees, K-Nearest Neighbor (KNN), Bagging and Boosting via C4.5, and Multi-Layer perceptron (MLP). The overall accuracy, precision, sensitivity, and F-measure verify that the proposed scheme can effectively distinguish between the VoIP flows and Non-VoIP ones in VPN traffic. |
| first_indexed | 2024-12-17T01:06:19Z |
| format | Article |
| id | doaj.art-507b88bffdd148edb26c45f484672eb9 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-17T01:06:19Z |
| publishDate | 2020-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-507b88bffdd148edb26c45f484672eb92022-12-21T22:09:14ZengAIMS PressMathematical Biosciences and Engineering1551-00182020-07-011754747477210.3934/mbe.2020260Identifying VoIP traffic in VPN tunnel via Flow Spatio-Temporal FeaturesFaiz Ul Islam0Guangjie Liu1Weiwei Liu21. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China2. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China1. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaThe persistent emergence of new network applications, along with encrypted network communication, has make traffic analysis become a challenging issue in network management and cyberspace security. Currently, virtual private network (VPNs) has become one of the most popular encrypted communication services for bypassing censorship and guarantee remote access to geographically locked services. In this paper, a novel identification scheme of VoIP traffic tunneled through VPN is proposed. We employed a set of Flow Spatio-Temporal Features (FSTF) to six well-known classifiers, including decision trees, K-Nearest Neighbor (KNN), Bagging and Boosting via C4.5, and Multi-Layer perceptron (MLP). The overall accuracy, precision, sensitivity, and F-measure verify that the proposed scheme can effectively distinguish between the VoIP flows and Non-VoIP ones in VPN traffic.https://www.aimspress.com/article/doi/10.3934/mbe.2020260?viewType=HTMLencrypted trafficflow spatio-temporal featuresmachine learningvirtual private network (vpn)voice over ip (voip) |
| spellingShingle | Faiz Ul Islam Guangjie Liu Weiwei Liu Identifying VoIP traffic in VPN tunnel via Flow Spatio-Temporal Features Mathematical Biosciences and Engineering encrypted traffic flow spatio-temporal features machine learning virtual private network (vpn) voice over ip (voip) |
| title | Identifying VoIP traffic in VPN tunnel via Flow Spatio-Temporal Features |
| title_full | Identifying VoIP traffic in VPN tunnel via Flow Spatio-Temporal Features |
| title_fullStr | Identifying VoIP traffic in VPN tunnel via Flow Spatio-Temporal Features |
| title_full_unstemmed | Identifying VoIP traffic in VPN tunnel via Flow Spatio-Temporal Features |
| title_short | Identifying VoIP traffic in VPN tunnel via Flow Spatio-Temporal Features |
| title_sort | identifying voip traffic in vpn tunnel via flow spatio temporal features |
| topic | encrypted traffic flow spatio-temporal features machine learning virtual private network (vpn) voice over ip (voip) |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2020260?viewType=HTML |
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