用对抗训练提升Wi-Fi定位抗欺骗和信号干扰能力
Adversarial-Ensemble Kolmogorov Arnold Networks for Enhancing Indoor Wi-Fi Positioning: A Defensive Approach Against Spoofing and Signal Manipulation Attacks
- 基于KAN架构，通过对抗样本训练增强模型鲁棒性
- 对抗训练使定位误差降低10%，攻击下误差降至2.00米
- 集成模型进一步优化，适合高安全需求的室内定位场景
本研究针对基于Wi-Fi的室内定位系统在对抗攻击下的脆弱性，提出三种模型：基准模型（M_Base）、对抗训练鲁棒模型（M_Rob）和集成模型（M_Ens），均采用柯尔莫哥洛夫-阿诺德网络（KAN）结构。鲁棒模型在对抗扰动数据上训练，集成模型融合基线与鲁棒模型预测。实验显示，鲁棒模型相比基线将定位误差降低约10%，在Wi-Fi欺骗攻击下误差为2.03米，在信号强度操纵攻击下为2.00米；集成模型表现更优，对应误差分别为2.01米和1.975米。结果表明，对抗训练能有效缓解攻击影响，强调在关键场景中考虑对抗威胁对提升定位精度与可靠性的重要性。
原文摘要 · Abstract (English)
The research presents a study on enhancing the robustness of Wi-Fi-based indoor positioning systems against adversarial attacks. The goal is to improve the positioning accuracy and resilience of these systems under two attack scenarios: Wi-Fi Spoofing and Signal Strength Manipulation. Three models are developed and evaluated: a baseline model (M_Base), an adversarially trained robust model (M_Rob), and an ensemble model (M_Ens). All models utilize a Kolmogorov-Arnold Network (KAN) architecture. The robust model is trained with adversarially perturbed data, while the ensemble model combines predictions from both the base and robust models. Experimental results show that the robust model reduces positioning error by approximately 10% compared to the baseline, achieving 2.03 meters error under Wi-Fi spoofing and 2.00 meters under signal strength manipulation. The ensemble model further outperforms with errors of 2.01 meters and 1.975 meters for the respective attack types. This analysis highlights the effectiveness of adversarial training techniques in mitigating attack impacts. The findings underscore the importance of considering adversarial scenarios in developing indoor positioning systems, as improved resilience can significantly enhance the accuracy and reliability of such systems in mission-critical environments.
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