ITC-Net-blend-60: a comprehensive dataset for robust network traffic classification in diverse environments
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a diverse mobile network traffic dataset to improve mobile application recognition in encrypted traffic. The dataset enhances the robustness and real-world applicability of network traffic classifiers.
Area Of Science
- Computer Science
- Network Security
- Data Science
Background
- Mobile application recognition in encrypted network traffic is crucial for network administration, security, and digital marketing.
- Existing datasets are limited as they originate from single network environments, hindering model evaluation for real-world robustness.
- Developing adaptable network traffic classifiers for dynamic settings remains a significant challenge.
Purpose Of The Study
- To address the limitations of current datasets by creating a more comprehensive and varied mobile network traffic dataset.
- To facilitate the development and evaluation of robust mobile application recognition models.
- To support research in network security and traffic analysis.
Main Methods
- Collected traffic data from 60 popular Android applications across five distinct network scenarios.
- Varied scenarios by changing Internet service provider (ISP), geographic location, device, application version, and user.
- Captured traffic via real human interactions on physical devices, excluding background traffic and without requiring root access.
Main Results
- The dataset contains over 48 million packets, 450,000 bidirectional flows, and 36 GB of data.
- Traffic was generated from 60 applications under diverse network conditions.
- Data collection method ensured practical applicability and avoided privileged access.
Conclusions
- The newly created dataset offers a valuable resource for training and validating mobile application recognition models.
- Its diversity across network conditions enhances the evaluation of classifier robustness and real-world performance.
- This work contributes to advancing network security and intelligent network management through improved traffic analysis.
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