Abstract: Wireless networks transmit data over open wireless channels, so they are vulnerable to impersonation attacks and information forgery attacks. To prevent such attacks, accurate device identification is required. The device identification technology based on channel state information (CSI) fingerprinting uses the wireless channel characteristics of device for identification. Since CSI can provide fine-grained channel characteristics and can be easily obtained from OFDM wireless devices, this technology has received wide attention. However, since CSI fingerprints identify the wireless channel characteristics of device, they change with the location or the environment of device. What’s more, the existing technologies usually use machine learning for fingerprint matching for increasing identification accuracy, but the computational complexity of fingerprint matching increases, which in turn cannot be implemented in embedded devices with limited computational ability. To address these problems, this paper proposes a hybrid feature fingerprint-based device identification scheme, which includes the identification in access stage and communication stage. Packet arrival interval distribution (PAID) fingerprint, which is independent of device’s location, is introduced for identification in access stage to compensate for the shortcomings of the CSI fingerprint. In communication stage, CSI fingerprints are extracted from each data packet and identified in real time with the feature that CSI can be acquired packet by packet. In addition, this paper proposes a fingerprint matching scheme with low computational complexity to ensure fast and accurate device identification even in devices with limited computational ability. We implement the identification system on Raspberry Pi and perform some experiments, which show that the identification accuracy is up to 98.17% and 98.7% in access stage and communication stage, and the identification time of a single packet in communication stage is only 0.142ms.