Biblio

Security is of vital importance in wireless industrial communication systems. When spoofing attacking has occurred, leading to economic losses or even safety accidents. So as to address the concern, existing approaches mainly rely on traditional cryptographic algorithms. However, these methods cannot meet the needs of short delay and lightweight. In this paper, we propose a CSI-based PHY-layer security authentication scheme to detect spoofing detection. The main idea takes advantage of the uncorrelated nature of wireless channels to the identification of spoofing nodes in the physical layer. We demonstrate a MIMO-OFDM based spoofing detection prototype in industrial environments. Firstly, utilizing Universal Software Radio Peripheral (USRPs) to establish MIMO-OFDM communication systems is presented. Secondly, our proposed security scheme of CSI-based PHY-layer authentication is demonstrated. Finally, the effectiveness of the proposed approach has been verified via attack experiments.

Recently, there has been significant enthusiasms in exploiting physical (PHY-) layer characteristics for secure wireless communication. However, most existing PHY-layer security paradigms are information theoretical methodologies, which are infeasible to real and practical systems. In this paper, we propose a weighted fractional Fourier transform (WFRFT) pre-coding scheme to enhance the security of wireless transmissions against eavesdropping. By leveraging the concept of WFRFT, the proposed scheme can easily change the characteristics of the underlying radio signals to complement and secure upper-layer cryptographic protocols. We demonstrate a running prototype based on the LTE-framework. First, the compatibility between the WFRFT pre-coding scheme and the conversational LTE architecture is presented. Then, the security mechanism of the WFRFT pre-coding scheme is demonstrated. Experimental results validate the practicability and security performance superiority of the proposed scheme.

Automatic modulation recognition (AMR) plays an important role in cognitive radio and electronic reconnaissance applications. In order to solve the problem that the lack of modulation signal data sets, the labeled data sets are generated by the software radio equipment NI-USRP 2920 and LabVIEW software development tool. In this paper, a combined network based on deep learning is proposed to identify ten types of digital modulation signals. Convolutional neural network (CNN) and Inception network are trained on different data sets, respectively. We combine CNN with Inception network to distinguish different modulation signals well. Experimental results show that our proposed method can recognize ten types of digital modulation signals with high identification accuracy, even in scenarios with a low signal-to-noise ratio (SNR).

In recent years, secret key generation based on physical layer security has gradually attracted high attentions. The wireless channel reciprocity and eavesdropping attack are critical problems in secret key generation studies. In this paper, we carry out a simulation and experimental study of channel reciprocity in terms of measuring channel state information (CSI) in both time division duplexing (TDD) and frequency division duplexing (FDD) modes. In simulation study, a close eavesdropping wiretap channel model is introduced to evaluate the security of the CSI by using Pearson correlation coefficient. In experimental study, an indoor wireless CSI measurement system is built with N210 and X310 universal software radio peripheral (USRP) platforms. In TDD mode, theoretical analysis and most of experimental results show that the closer eavesdropping distance, the higher CSI correlation coefficient between eavesdropping channel and legitimate channel. However, in actual environment, when eavesdropping distance is too close (less than 1/4 wavelength), this CSI correlation seriously dropped. In FDD mode, both theoretical analysis and experimental results show that the wireless channel still owns some reciprocity. When frequency interval increases, the FDD channel reciprocity in actual environment is better than that in theoretical analysis.

In this paper a new physical layer security method is proposed against eavesdropping attacks. Our purpose is to demonstrate that performance of the legitimate receiver can be increased and performance of the eavesdropper can be decreased by matching between the roll of factors of root raised cosine filters in the transmitter and receiver. Through the matching between the roll of factors (a), a performance difference is generated between the legitimate receiver and the eavesdropper. By using three software defined radio nodes error vector magnitude of the legitimate receiver and the eavesdropper is measured according to roll of factors. Performance differences the receiver are demonstrated when the roll off factor is matched and mismatched.