arXiv：cs.LG· Mamady Delamou, Luan Chen, Arsenia Chorti·· 10 小时前
基于自适应高斯混合模型的物理层异常检测
Physical Layer Anomaly Detection via Adaptive Gaussian Mixture Model
AI 导读
针对室内无线定位中攻击者与合法节点空间邻近时 CSI 特征高度相似的问题，研究提出基于自适应高斯混合模型（GMM）的检测框架，通过学习合法 CSI 模式并做统计异常检测来识别空间不一致。
正文
Abstract:Physical-layer spoofing attacks pose a critical threat to wireless localization systems by manipulating channel state information (CSI). Detecting such attacks becomes particularly challenging when the attacker and the legitimate node are spatially proximate, as their CSI characteristics become increasingly similar. To address this challenge in indoor scenarios, we propose an adaptive Gaussian mixture model (GMM)-based detection framework that learns legitimate CSI patterns and detects spatial inconsistency through statistical anomaly detection. Using a massive multiple-input multiple-output (MIMO) experimental dataset with 64 uniform linear array (ULA) antenna and 5 mm spatial resolution, we evaluated our detector against CSI spoofing attacks with spatial displacements ranging from 50 mm to 450 mm. The adaptive GMM achieves 95-98% detection rate for displacements above 150 mm while maintaining 1% false alarm rate. We benchmark our approach against conventional GMM, local outlier factor (LOF), one-class support vector machine (SVM) and k-nearest neighbors (k-NN) classifiers, demonstrating overall superior detection performance of our proposed classifier.
| Comments: | 7 figures et 1 tableau |
| Subjects: | Cryptography and Security (cs.CR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.18946 [cs.CR] |
| (or arXiv:2610.18946v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18946 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mamady Delamou [view email]
[v1]
Tue, 13 Oct 2026 15:04:58 UTC (135 KB)
来源：arXiv：cs.LG · arxiv.org