发表机构
Faculty of Electrical and Electronics Engineering, Istanbul Technical University; TUBITAK BILGEM（伊斯坦布尔理工大学电气与电子工程学院; 土耳其科学技术研究理事会信息科学与安全研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种结合可解释人工智能的O-RAN网络异常检测框架，通过事后可解释性方法将数据集复杂度降低80%且不损准确率，可识别关键攻击特征，平衡效率、准确率与可解释性，适用于NextG网络安全。
AI 中文摘要
无线网络历来面临严重的安全漏洞，因此需要先进的异常检测机制，尤其是在网络向6G及更未来版本演进的背景下。本研究引入一种先进的异常检测框架，该框架利用可解释人工智能来增强下一代（NextG）蜂窝网络的安全性。通过在实际开放无线接入网（O-RAN）测试环境中实现并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准确率和高效的运行时性能。本研究的一项关键创新是集成事后可解释性方法，以识别最关键的关键性能指标（KPMs），这可在不降低检测准确率的情况下将数据集复杂度显著降低80%。此外，可解释性分析还识别出若干关键攻击流量特征，如协议类型、带宽、间隔和持续时间，以防范即将到来的网络攻击。最终的框架有效平衡了计算效率、准确率和可解释性，凸显了其在增强下一代蜂窝网络安全性方面的实际适用性。
英文摘要
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.
CommentsAccepted in IEEE WCNC 2026, Copyright IEEE
