用深度森林解决物联网恶意流量检测中的数据不平衡问题。
Network Threat Detection: Addressing Class Imbalanced Data with Deep Forest
- 采用三种重采样策略缓解数据不平衡问题。
- gcForest在检测准确率上优于传统机器学习方法。
- 适合研究物联网安全与不平衡数据处理的读者。
随着物联网（IoT）网络的快速扩展，实时检测恶意流量已成为关键的网络安全挑战。本研究基于斯特拉托斯菲亚实验室提供的IoT-23数据集，对多种机器学习技术在恶意软件检测中的应用进行了全面的实证分析。针对数据集中显著的类别不平衡问题，采用了三种重采样策略。通过实现并比较多种机器学习方法，结果表明：将适当的不平衡处理技术与集成方法（特别是gcForest）结合，相比传统方法能获得更优的检测性能。该工作为构建更智能、高效的物联网环境自动化威胁检测系统提供了重要支持，有助于保护关键基础设施免受复杂网络攻击，同时优化计算资源使用。
原文摘要 · Abstract (English)
With the rapid expansion of Internet of Things (IoT) networks, detecting malicious traffic in real-time has become a critical cybersecurity challenge. This research addresses the detection challenges by presenting a comprehensive empirical analysis of machine learning techniques for malware detection using the IoT-23 dataset provided by the Stratosphere Laboratory. We address the significant class imbalance within the dataset through three resampling strategies. We implement and compare a few machine learning techniques. Our findings demonstrate that the combination of appropriate imbalance treatment techniques with ensemble methods, particularly gcForest, achieves better detection performance compared to traditional approaches. This work contributes significantly to the development of more intelligent and efficient automated threat detection systems for IoT environments, helping to secure critical infrastructure against sophisticated cyber attacks while optimizing computational resource usage.
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