用可解释AI提升6G网络异常检测，精准识别恶意流量
Explainability Boosted Anomaly Detection Framework for O-RAN based NextG Networks
- 结合后处理解释方法，筛选关键性能指标
- 数据集复杂度降低80%仍保持高检测准确率
- 揭示攻击特征如协议类型、带宽等，适合安全团队使用
无线网络长期存在显著安全漏洞，随着向6G及更远演进，亟需先进的异常检测机制。本研究提出一种基于可解释人工智能的先进异常检测框架，用于增强下一代（NextG）蜂窝网络的安全性。通过在真实开放无线接入网（O-RAN）测试环境中部署与评估多种人工智能模型，该框架在识别恶意流量方面展现出高精度与高效运行性能。其关键创新在于集成事后可解释性方法，识别出最关键的性能指标（KPMs），在不牺牲检测准确率的前提下，实现数据集复杂度80%的显著降低。此外，可解释性分析揭示了多个关键攻击流量特征，如协议类型、带宽、间隔和持续时间，有助于预防未来网络攻击。该框架有效平衡了计算效率、准确性与可解释性，凸显其在下一代蜂窝网络安全增强中的实际应用价值。
原文摘要 · Abstract (English)
The wireless networks have historically faced significant security vulnerabilities, necessitating advanced anomaly detection mechanisms, especially as networks evolve towards 6G and beyond. This study introduces an advanced anomaly detection framework that leverages explainable artificial intelligence to enhance the security of next-generation (NextG) cellular networks. By implementing and evaluating a variety of artificial intelligence models, the framework demonstrates high accuracy and efficient runtime performance in identifying malicious traffic within a realistic Open Radio Access Network (O-RAN) testbed. A key innovation of this work is the integration of post-hoc explainability methods to identify the most critical key performance metrics (KPMs), which enables a significant 80% reduction in dataset complexity without compromising detection accuracy. Additionally, explainability analyses identify several critical attack traffic characteristics, such as protocol type, bandwidth, interval, and duration, to prevent upcoming network attacks. The resulting framework effectively balances computational efficiency, accuracy, and explainability, underscoring its practical applicability for enhancing security in next-generation cellular networks.
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