A deep learning‐based framework to identify and characterise heterogeneous secure network traffic
Abstract The evergrowing diversity of encrypted and anonymous network traffic makes network management more formidable to manage the network traffic. An intelligent system is essential to analyse and identify network traffic accurately. Network management needs such techniques to improve the Quality...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-03-01
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| Series: | IET Information Security |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ise2.12095 |
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| _version_ | 1832546766571962368 |
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| author | Faiz Ul Islam Guangjie Liu Weiwei Liu Qazi Mazhar ul Haq |
| author_facet | Faiz Ul Islam Guangjie Liu Weiwei Liu Qazi Mazhar ul Haq |
| author_sort | Faiz Ul Islam |
| collection | DOAJ |
| description | Abstract The evergrowing diversity of encrypted and anonymous network traffic makes network management more formidable to manage the network traffic. An intelligent system is essential to analyse and identify network traffic accurately. Network management needs such techniques to improve the Quality of Service and ensure the flow of secure network traffic. However, due to the usage of non‐standard ports and encryption of data payloads, the classical port‐based and payload‐based classification techniques fail to classify the secured network traffic. To solve the above‐mentioned problems, this paper proposed an effective deep learning‐based framework employed with flow‐time‐based features to predict heterogeneous secure network traffic best. The state‐of‐the‐art machine learning strategies (C4.5, random forest, and K‐nearest neighbour) are investigated for comparison. The proposed 1D‐CNN model achieved higher accuracy in classifying the heterogeneous secure network traffic. In the next step, the proposed deep learning model characterises the major categories (virtual private network traffic, the onion router network traffic, and plain encrypted network traffic) into several application types. The experimental results show the effectiveness and feasibility of the proposed deep learning framework, which yields improved predictive power compared to the state‐of‐the‐art machine learning techniques employed for secure network traffic analysis. |
| format | Article |
| id | doaj-art-cca7b45a5d404f00bf2887f1411d7161 |
| institution | Kabale University |
| issn | 1751-8709 1751-8717 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Information Security |
| spelling | doaj-art-cca7b45a5d404f00bf2887f1411d71612025-02-03T06:47:17ZengWileyIET Information Security1751-87091751-87172023-03-0117229430810.1049/ise2.12095A deep learning‐based framework to identify and characterise heterogeneous secure network trafficFaiz Ul Islam0Guangjie Liu1Weiwei Liu2Qazi Mazhar ul Haq3School of Automation Nanjing University of Science and Technology Nanjing ChinaSchool of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing ChinaSchool of Automation Nanjing University of Science and Technology Nanjing ChinaDepartment of Computer Software Engineering, Military College of Signals National University of Science and Technology Islamabad PakistanAbstract The evergrowing diversity of encrypted and anonymous network traffic makes network management more formidable to manage the network traffic. An intelligent system is essential to analyse and identify network traffic accurately. Network management needs such techniques to improve the Quality of Service and ensure the flow of secure network traffic. However, due to the usage of non‐standard ports and encryption of data payloads, the classical port‐based and payload‐based classification techniques fail to classify the secured network traffic. To solve the above‐mentioned problems, this paper proposed an effective deep learning‐based framework employed with flow‐time‐based features to predict heterogeneous secure network traffic best. The state‐of‐the‐art machine learning strategies (C4.5, random forest, and K‐nearest neighbour) are investigated for comparison. The proposed 1D‐CNN model achieved higher accuracy in classifying the heterogeneous secure network traffic. In the next step, the proposed deep learning model characterises the major categories (virtual private network traffic, the onion router network traffic, and plain encrypted network traffic) into several application types. The experimental results show the effectiveness and feasibility of the proposed deep learning framework, which yields improved predictive power compared to the state‐of‐the‐art machine learning techniques employed for secure network traffic analysis.https://doi.org/10.1049/ise2.12095deep learningencrypted network trafficmachine learningnetwork traffic classificationTOR networkvirtual private network (VPN) |
| spellingShingle | Faiz Ul Islam Guangjie Liu Weiwei Liu Qazi Mazhar ul Haq A deep learning‐based framework to identify and characterise heterogeneous secure network traffic IET Information Security deep learning encrypted network traffic machine learning network traffic classification TOR network virtual private network (VPN) |
| title | A deep learning‐based framework to identify and characterise heterogeneous secure network traffic |
| title_full | A deep learning‐based framework to identify and characterise heterogeneous secure network traffic |
| title_fullStr | A deep learning‐based framework to identify and characterise heterogeneous secure network traffic |
| title_full_unstemmed | A deep learning‐based framework to identify and characterise heterogeneous secure network traffic |
| title_short | A deep learning‐based framework to identify and characterise heterogeneous secure network traffic |
| title_sort | deep learning based framework to identify and characterise heterogeneous secure network traffic |
| topic | deep learning encrypted network traffic machine learning network traffic classification TOR network virtual private network (VPN) |
| url | https://doi.org/10.1049/ise2.12095 |
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