Synthetic Network Spoofing Detection Dataset: 100,000 Traffic Records for AI-Based Multi-Layer Spoofing Detection

This dataset contains 100,000 synthetic network traffic records generated to evaluate AI-based multi-layer spoofing detection systems. The dataset encompasses five attack categories: IP spoofing, MAC spoofing, ARP spoofing, DNS spoofing, and DDoS attacks, alongside normal traffic (85,000 records). Each record contains 45 engineered features derived from network-level, device-level, and behavioral-level attributes.

Files included:- raw_traffic.csv: Raw simulated network traffic- labeled_dataset.csv: Fully labeled and feature-engineered dataset- train_data.csv: Training split (70,000 records)- validation_data.csv: Validation split (15,000 records)- test_data.csv: Test split (15,000 records)- device_profiles.csv: Per-device behavioral profiles (500 devices)- threat_intelligence.csv: Temporal threat intelligence sequences- cyber.ipynb: Dataset generation and preprocessing notebook

This dataset was used in the paper: "AI-Based Multi-Layer Spoofing Detection System Using Deep Learning and Adaptive Policy Networks for Real-Time Network Security," submitted to Springer Telecommunication Systems Journal, 2026.

本数据集包含100,000条合成网络流量记录，用于评估基于人工智能（AI）的多层欺骗检测系统。本数据集涵盖五类攻击类别：IP欺骗、MAC欺骗、ARP欺骗、DNS欺骗与分布式拒绝服务（DDoS）攻击，同时包含正常流量记录（共计85,000条）。每条记录包含45项经工程化处理的特征，提取自网络级、设备级与行为级属性。

包含的文件如下：
- raw_traffic.csv：原始模拟网络流量文件
- labeled_dataset.csv：全标注且已完成特征工程的数据集
- train_data.csv：训练集（70,000条记录）
- validation_data.csv：验证集（15,000条记录）
- test_data.csv：测试集（15,000条记录）
- device_profiles.csv：单设备行为轮廓（共500台设备）
- threat_intelligence.csv：时序威胁情报序列文件
- cyber.ipynb：数据集生成与预处理笔记本

本数据集曾用于2026年提交至Springer《Telecommunication Systems Journal》的论文《基于深度学习与自适应策略网络的实时网络安全多层欺骗检测系统》（AI-Based Multi-Layer Spoofing Detection System Using Deep Learning and Adaptive Policy Networks for Real-Time Network Security）。