A Novel Dataset for Multiclass Detection and Classification of Darknet Traffic (SafeSurf Darknet 2025)
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📦 Dataset Title SafeSurf Darknet 2025: A Multi-layer Behavioral Dataset for Darknet Traffic Detection and Classification 📘 Dataset Description SafeSurf Darknet 2025 is a richly labeled dataset that captures network traffic across various anonymizing technologies and VPNs. Unlike traditional datasets labeled by ports or protocols, this dataset organizes traffic by behavioral context, enabling advanced research in: Darknet behavior classification Encrypted traffic analysis Intrusion and anomaly detection systems (IDS/ADS) 🧪 Labeling and Data Collection Methodology All traffic was manually generated and labeled in a controlled environment. Sessions were classified based on the known context of user activity (e.g., watching YouTube over Tor = Video Streaming). Key aspects: Manual labeling only – no heuristic or automated labeling was used Behavior isolation through dedicated setups and time-aligned collection High-confidence ground-truth annotations 🔐 Privacy-Preserving Technologies Covered Tor Freenet I2P ZeroNet VPN 🎯 Behavioral Classes Captured Browsing Email Chatting Voice over IP (VoIP) File Transfer (FTP) Audio Streaming Video Streaming Peer-to-Peer (P2P) Sharing Normal (non-darknet traffic) Note: Some behaviors (e.g., VOIP) are not captured across all technologies due to service limitations. 🧩 Dataset Structure Layer 1 – Binary Labeling Normal: 360,358 samples Darknet: 91,404 samples Layer 2 – Technology-Specific Classification Freenet: 26,284 samples ZeroNet: 25,499 samples I2P: 22,958 samples Tor: 12,546 samples VPN: 4,117 samples Layer 3 – Behavioral Labeling Browsing: 33,586 FTP: 20,214 Video Streaming: 9,559 P2P Sharing: 9,392 Email: 7,873 Audio Streaming: 5,953 Chatting: 3,489 VOIP: 1,338 📄 Data Format and Features Provided in CSV format, where each row represents a single network flow, labeled with its associated behavior. Feature columns include: Timestamps Flow duration Packet and byte counts Inter-arrival time metrics TCP/UDP header statistics Directional and flow-based indicators This structure supports a wide range of machine learning and network security research. 💡 Use Cases Ideal for research and development in: Behavior-based IDS/ADS Real-time encrypted traffic detection Behavioral profiling across anonymizing technologies Multi-class classification under behavioral and technological variance 📚 Citation and Licensing Please cite the dataset in your publications and respect the licensing terms included in the dataset repository. 📁 Access the dataset here: 🔗 Mendeley Data – SafeSurf Darknet 2025 📄 Related Publications: 🔗 https://www.preprints.org/manuscript/202507.1926/v1 🔗 https://ieeexplore.ieee.org/abstract/document/11073091
📦 数据集标题 SafeSurf Darknet 2025：面向暗网流量检测与分类的多层行为数据集 📘 数据集描述 SafeSurf Darknet 2025是一款标注完备的高质量数据集，收录了基于多种匿名化技术与虚拟专用网络（Virtual Private Network，简称VPN）的网络流量。与传统基于端口或协议进行标注的数据集不同，本数据集以行为上下文对流量进行组织，可支撑以下前沿研究方向： - 暗网行为分类 - 加密流量分析 - 入侵与异常检测系统（IDS/ADS） 🔬 标注与数据采集方法 所有流量均在受控环境中手动生成并标注，会话基于已知的用户活动上下文进行分类（例如，通过Tor访问YouTube对应视频流服务）。 核心要点： - 仅采用人工标注，未使用任何启发式或自动化标注手段 - 通过专用部署与时间对齐采集实现行为隔离 - 具备高置信度的基准真值标注 🔐 覆盖的隐私保护技术 - Tor - Freenet - I2P - ZeroNet - 虚拟专用网络（Virtual Private Network，简称VPN） 🎯 收录的行为类别 - 网页浏览 - 电子邮件 - 即时聊天 - 互联网语音协议（Voice over IP，简称VoIP） - 文件传输协议（File Transfer Protocol，简称FTP） - 音频流传输 - 视频流传输 - 点对点（Peer-to-Peer，简称P2P）共享 - 正常流量（非暗网流量） 注：由于服务限制，部分行为（例如VoIP）并非在所有匿名化技术中均有收录。 🧩 数据集结构 第一层：二元标注 - 正常流量样本：360,358条 - 暗网流量样本：91,404条 第二层：技术专属分类 - Freenet：26,284条 - ZeroNet：25,499条 - I2P：22,958条 - Tor：12,546条 - 虚拟专用网络（Virtual Private Network，简称VPN）：4,117条 第三层：行为标注 - 网页浏览：33,586条 - 文件传输协议（FTP）：20,214条 - 视频流传输：9,559条 - 点对点（P2P）共享：9,392条 - 电子邮件：7,873条 - 音频流传输：5,953条 - 即时聊天：3,489条 - 互联网语音协议（VoIP）：1,338条 📄 数据格式与特征 数据集以逗号分隔值（CSV）格式存储，每行对应一条独立的网络流，并标注其所属行为类别。特征字段包括： - 时间戳 - 流持续时长 - 数据包与字节数统计 - 包间间隔指标 - 传输控制协议（Transmission Control Protocol，简称TCP）/用户数据报协议（User Datagram Protocol，简称UDP）头部统计特征 - 流向与基于流的指标 该结构可支撑多类机器学习与网络安全相关研究。 💡 应用场景 适用于以下方向的研究与开发： - 基于行为的入侵与异常检测系统（IDS/ADS） - 实时加密流量检测 - 基于匿名化技术的行为画像 - 存在行为与技术差异下的多分类任务 📚 引用与许可 请在您的学术成果中引用该数据集，并遵守数据集仓库中包含的许可条款。 📁 数据集获取链接： 🔗 Mendeley数据平台——SafeSurf Darknet 2025 📄 相关出版物： 🔗 https://www.preprints.org/manuscript/202507.1926/v1 🔗 https://ieeexplore.ieee.org/abstract/document/11073091
