Research On Radio Frequency Fingerprint Extraction And Recognition For Multi-standard Wireless Communication Protocols

In recent years,wireless communication technology has developed rapidly,playing an important role in both military and civilian applications,and has profoundly changed all walks of life.However,the openness of wireless communication makes it more vulnerable to illegal attacks such as device counterfeiting,information tampering and data forgery,which brings serious security problems.An important element of wireless communication security is wireless access security,and identity authentication is the first task of wireless access security.Radio frequency fingerprint(RFF)technology is an effective authentication solution.Due to the tolerance of design and manufacturing process,different devices have different hardware differences and are reflected in the radio frequency(RF)signals.RFF technology has enhanced the security of wireless communication applications by analyzing RF signals and extracting relevant hardware differences as features to form fingerprints for device identification and access authentication.Therefore,conducting RF fingerprinting research on wireless communication physical layer security has profound theoretical significance and significant practical value in both military and civilian fields.In this thesis,we focus on RFF extraction and recognition for multi-standard wireless communication protocols mainly including the general and stable RFF extraction and recognition in multi-standard wireless communication protocols,robust RFF extraction and recognition in complex channels,RFF extraction and recognition with insufficient samples,and the open set recognition problem.The proposed algorithms are verified with five communication protocols including ZigBee,GSM,Wi-Fi,LoRa and LTE.The innovative achievements of this thesis include:1.A general and stable RFF extraction and recognition method for multi-standard wireless communication protocols is proposed.In order to improve the generality and stability of RFF technology for multi-standard wireless communication protocols,this thesis proposes a long-term stacking of repetitive symbols(LSRS)algorithm.LSRS utilizes the widely existing repetitive signals for stacking,which has reduced the influence of time-varying factors to improve the stability of RFF.In addition,the theoretical derivation demonstrates that LSRS can lead to SNR improvement and reduce the impact of SNR fluctuation on fingerprint stability.The algorithm has achieved a recognition rate of 100% in the experiments of ZigBee,Wi-Fi and LTE.In addition,the performance loss is less than 0.8% in the 18 month data set of 54 ZigBee devices.2.A robust RF fingerprint extraction and recognition method with low SNR is proposed.In order to improve the robustness of RFF technique with low SNR in AWGN channel,an artificial noise adding(ANA)algorithm is proposed in this thesis.ANA makes the training signal and test signal have similar SNR by adding artificial noise to the training signal to extract the discriminative hardware features under this SNR to form the RFF.The traditional robust RFF algorithm needs to be trained under each SNR,while ANA uses theoretical calculation to add noise adaptively according to the channel condition of the received signal,which consumes low resources and has significant practical significance.The experimental results show that the recognition rate of 54 ZigBee devices is 91.82% in the 0d B AWGN channel scenario,and the recognition rate can be maintained near 100% when the SNR is in 10 d B-26 d B.3.A channel robust RFF extraction and recognition method based on Mahalanobis distance equalization is proposed.Wireless communication devices need to work in different channels in practical applications,where the fading of multipath channels can seriously affect the extraction and recognition of fingerprints.Based on this,this thesis proposes a channel robust RFF extraction and recognition method based on Mahalanobis distance equalization(MDE)algorithm.The linear filter is difficult to equalize the nonlinearity of RFF.Therefore,MDE can recognize devices under multipath channels by controlling the parameters of the filter and using the Mahalanobis distance as the loss function to equalize channel fading while maintaining the RFF features.Simulation experiments with 54 ZigBee devices show that this method can effectively improve the robustness under multipath channels.In real-world experiments,MDE shows a 16.21% recognition rate improvement over the conventional scheme in the ZigBee dataset under long range line-of-sight channels.In addition,the recognition rate is improved by up to 82.86% on the multi-location dataset with 16 Wi-Fi devices.4.A channel robust RFF extraction and recognition method based on frequency domain quotient is proposed.When the channel is more complex,linear filters may not be able to equalize the channel effectively.Based on this,a channel robust RFF extraction and recognition method based on frequency domain quotient(FDQ)is proposed in this thesis.Different data symbols express the hardware characteristics from different perspectives,and adjacent data symbols have similar channel characteristics in the channel coherence time.Therefore,it is theoretically possible to construct frequency domain quotient using adjacent symbols with different data to retain certain fingerprints while eliminating the channel.In this thesis,this method is evaluated with a dataset of 20 Wi-Fi devices at different locations,and the experimental results show that the algorithm is able to achieve a recognition rate of more than 99% even when the channels of training signals and test signals are different.5.A sub-sequence aggregation based RFF extraction and recognition method is proposed.Limited by practical resources,it is difficult to collect sufficient training samples for some complex communication protocols,which may lead to unstable performance of the RFF scheme when processing uncollected data.Cyclic shift sequences are widely used in communication protocols,and this thesis proposes a sub-sequence aggregation based RFF extraction and recognition method for cyclic shift sequences with insufficient samples.This method utilizes the cyclic shift property to construct coupled sub-sequences.The coupled sub-sequences are similar as they are hardware responses to the same data in the cyclic shift domain.Using this coupling relationship,this method requires only part of sequence types to recognize all types of cyclic shift sequence.Experimental results in ZigBee,LTE and LoRa have proved that this method is of practical importance as it only needs to collect a small amount of data to achieve similar performance as when collecting all types of data,reducing the requirement for data collection and improving data utilization.6.A generative model based open set RFF recognition technique is proposed.The practical application of RFF needs to face the access and interference of unknown devices.In this regard,this thesis studies the open set identification problem,and proposes a generative model based open set RFF recognition technique with reference to the research results of other fields.The technique decomposes the feature vector of the received signal into device identity vector and noise vector and tries to model the generation process,so that the identification of unknown devices is transformed into a simple hypothesis test.In the open set experiments with six legitimate ZigBee devices and six unknown devices,the equal error rate(EER)is only 0.63%,which is much lower than the deep learning based technique.