用可解释AI提升6G物联网安全，让模型决策更透明可信。
An Approach To Enhance IoT Security In 6G Networks Through Explainable AI
- 基于树模型处理复杂数据，识别关键安全特征。
- 结合SHAP/LIME提升模型透明度，准确率显著提高。
- 适合关注6G安全与AI可解释性的研究人员和工程师。
无线通信已取得显著进展，6G为物联网（IoT）提供了突破性能力。然而，物联网与6G的融合带来了新的安全挑战，攻击面因开放无线接入网（open RAN）、太赫兹（THz）通信、智能反射表面（IRS）、大规模多输入多输出（massive MIMO）及人工智能等先进技术引入的漏洞而扩大。新兴威胁如AI滥用、虚拟化风险以及数据篡改、信号干扰等演化攻击进一步加剧了安全难题。随着6G标准预计在2030年定稿，安全措施需与技术发展同步推进，但现有针对集成式6G-IoT系统的安全框架仍存在明显空白。本研究通过树基机器学习算法处理复杂数据集并评估特征重要性，采用数据平衡技术确保攻击类型公平表示，并结合SHAP与LIME方法增强模型可解释性。通过将特征重要性与可解释人工智能（XAI）方法对齐，并进行交叉验证以保证一致性，显著提升了模型准确率，从而强化了6G生态系统中的物联网安全防护能力。
原文摘要 · Abstract (English)
Wireless communication has evolved significantly, with 6G offering groundbreaking capabilities, particularly for IoT. However, the integration of IoT into 6G presents new security challenges, expanding the attack surface due to vulnerabilities introduced by advanced technologies such as open RAN, terahertz (THz) communication, IRS, massive MIMO, and AI. Emerging threats like AI exploitation, virtualization risks, and evolving attacks, including data manipulation and signal interference, further complicate security efforts. As 6G standards are set to be finalized by 2030, work continues to align security measures with technological advances. However, substantial gaps remain in frameworks designed to secure integrated IoT and 6G systems. Our research addresses these challenges by utilizing tree-based machine learning algorithms to manage complex datasets and evaluate feature importance. We apply data balancing techniques to ensure fair attack representation and use SHAP and LIME to improve model transparency. By aligning feature importance with XAI methods and cross-validating for consistency, we boost model accuracy and enhance IoT security within the 6G ecosystem.
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