Abstract: Aiming at the problem of low accuracy rate of existing low-power Bluetooth spoofing attack detection techniques,a BLE spoofing attack detection technique based on anomalous fingerprints is proposed,which takes the attacker's RF fingerprints as anomalous data and models spoofing attack detection as an anomalous detection problem.This paper designs an anomalous fingerprint detection model—RFFAD_DeepSVDD,based on deep support vector data description(DeepSVDD),which uses the residual unit to construct a network model,effectively alleviating the problem of insufficient nonlinear feature extraction of machine learning anomaly detection algorithm.Pre-trained auto-encoder is used to obtain the optimal initialization parameter,which greatly enhances the model's boundary decision-making ability.In the anomaly detection experiments,the accuracy of the model reaches 95.47%,an average improvement of 8.92% compared with the machine learning-based anomaly detection model;in the spoofing attack detection experiments,compared with the existing spoofing attack detection techniques in the attack node movement and stationary state,the proposed method shows better performance,and can accurately detect and identify man-in-the-middle attack,impersonation attack,and reconnection spoofing attackthree kinds of spoofing attacks.

Key words: BLE, Spoofing attack, RF fingerprint, Anomaly detection, DeepSVDD