適応的ガウス混合モデルによる物理層異常検知
Physical Layer Anomaly Detection via Adaptive Gaussian Mixture Model
無線測位システムへの物理層スプーフィング攻撃に対し、適応的ガウス混合モデルで正規のCSIパターンを学習し統計的異常検知を行う手法を提案し、150mm以上の空間変位で95-98%の検出率を達成した。
著者: Mamady Delamou, Luan Chen, Arsenia Chorti
分類: cs.CR, cs.LG
原文アブストラクト
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.