Exploiting the reciprocity and randomness of the wireless channel for secret key generation (SKG) is a promising technique, which attracts great interests recently toward physical layer security. Although various methods have been developed for efficient SKG, the key disagreement rate (KDR) performance is still difficult to be ensured when the transmission channel is with severe interference and noise. To alleviate this problem, this paper presents an efficient channel information extraction algorithm based on extracting effective channel paths to reduce the KDR. Simulation results demonstrate that the proposed algorithm provides significant improvement compared with the existing approaches.
Exploiting the reciprocity and randomness of the wireless channel for secret key generation (SKG) is a promising technique, which attracts great interests recently toward physical layer security. Although various methods have been developed for efficient SKG, the key disagreement rate (KDR) performance is still difficult to be ensured when the transmission channel is with severe interference and noise. To alleviate this problem, this paper presents an efficient channel information extraction algorithm based on extracting effective channel paths to reduce the KDR. Simulation results demonstrate that the proposed algorithm provides significant improvement compared with the existing approaches.
The Secret Key Generation (SKG) scheme that exploits the reciprocity and uniqueness of wireless channel between two users plays a significant part in a new increasing distributed security system. The scheme performance can be distinguished based on the low value of Key disagreement Rate (KDR), the high value of Key Generation Rate (KGR), as well as the fulfillment of the NIST randomness standard. The previous SKG scheme has a high KDR due to a direct quantization of a measurement result of the Received Signal Strength (RSS). To overcome the above issue, we conduct a pre-processing of measurement result before quantization with the Kalman method. The pre-process is carried out to improve the channel reciprocity between two legitimate users with the objective to reduce the bit mismatch. Through an experiment, we propose a new quantization scheme called a Modified Multi-Bit (MMB) that uses a multi-bit system on every level of quantization. The test results show that the proposed combination of preprocessing and the MMB scheme has a better performance compared to the existing schemes in terms of KDR and KGR. The Secret Key generated by our scheme also fulfills the NIST randomness standard.
With the wide application of wireless sensor network, the security mechanism based on cryptography faces many challenges in computational complexity and security, and physical layer secret key extraction as an alternative solution has been widely studied. To meet the requirements of high key generation rate (KGR) and low key disagreement rate (KDR) in the secret key extraction process, an improved channel quantization alternating (CQA) algorithm is proposed in this paper. The algorithm filtrates the phase values of channel frequency response (CFR) using test results of CFR amplitude values, and then quantizes the reserved phase values by CQA algorithm. Compared to the existing CQA algorithm and improved channel quantization with guard-band (CQG) algorithm, simulation results show that the KDR of the proposed algorithm decreases by 31.5% and 20.4%, and the KGR decreases by 5.4% and increases by 5.2%, respectively. The proposed algorithm improves the KGR while ensuring a low KDR, and the extracted secret key passes the NIST test.
Physical layer encryption is a promising solution to provide transmission security for Internet-of-Things (IoT). To realize physical layer encryption, channel state information (CSI) as a random source to generate secret keys should be exploited. The reciprocity of wireless channel between legitimate communication nodes which is typically assumed for time division duplex (TDD) systems, is of critical importance to ensure the consistency in generated keys at two nodes. However, in practical TDD systems, due to the asynchronous measurement of CSI, the receiver noise and the the difference in hardware, etc., the reciprocity of channel measurements between legitimate parties is hard to be guaranteed, which makes physical layer key generation approach infeasible. To address this issue, we propose a wireless key generation scheme based on deep learning techniques. Firstly, a feature extraction network, which consists of two auto-encoders trained jointly, is developed to extract the common feature from the different yet correlated channel observations at two legitimate nodes, which will be used as the source to generate keys. Then a quantizer with an adaptive guard band width is devised to output a binary secret-key sequence from the feature vector. Numerical results show that the proposed scheme achieves remarkable improvement in terms of key disagreement rate (KDR) and key generation rate (KGR) compared existing counterparts in the literature.
The basis of generating secret key from the common wireless channel at two communication parties is reciprocity. However, due to non-simultaneous measurements and unavoidable estimation error, bias always exists and key disagreement occurs. In this paper, performance analysis and improvements for key generation exploiting imperfect channel statement information (CSI) is studied. Firstly, the closed-form expression for key disagreement rate (KDR) is derived to evaluate the key mismatch caused by time delay and estimation error. Secondly, the energy efficiency involving power allocation between key generation and private data transmission is presented. After that, a neural network based prediction (NNBP) algorithm is proposed to improve the key consistency. Simulation results verify the theoretical analysis and show that NNBP achieves significant improvements. And it's interesting to find that NNBP algorithm achieves a higher performance gain in low signal-to-noise ration (SNR) and high delay condition, which is different from other methods, like guard band based quantizer (GBBQ).
The basis of generating secret key from the common wireless channel at two communication parties is reciprocity. However, due to non-simultaneous measurements and unavoidable estimation error, bias always exists and key disagreement occurs. In this paper, performance analysis and improvements for key generation exploiting imperfect channel statement information (CSI) is studied. Firstly, the closed-form expression for key disagreement rate (KDR) is derived to evaluate the key mismatch caused by time delay and estimation error. Secondly, the energy efficiency involving power allocation between key generation and private data transmission is presented. After that, a neural network based prediction (NNBP) algorithm is proposed to improve the key consistency. Simulation results verify the theoretical analysis and show that NNBP achieves significant improvements. And it's interesting to find that NNBP algorithm achieves a higher performance gain in low signal-to-noise ration (SNR) and high delay condition, which is different from other methods, like guard band based quantizer (GBBQ).
The rapid technological advancements in wireless networks have resulted in tremendous increase in the number of connected user and machine-type wireless devices. Physical Layer Security (PLS) has a strong potential for providing security to wireless links in massively connected future wireless networks. The key-based PLS methods aim at generating secret key bits for data encryption/decryption at the legitimate communication nodes by exploiting the randomness inherent in wireless channels. This work proposes a novel secret key-bits extraction algorithm named Moving-Window Wrapped-Envelope Secret Key Generation (MWWE-SKG). The proposed algorithm uses first-order statistics from a sliding temporal window of channel samples to select the channel quantization interval for each window from circularly wrapped channel envelope samples. The differences in hardware noise conditions at the legitimate nodes lead to differences in the measurement of ideally reciprocal channels, which further leads to a mismatch in the generated secret key bits, whereas the channel statistics of the same links are more likely to remain unchanged for a reasonably large length of time window over the channel samples. An extensive analysis on the performance of the proposed algorithm is conducted, where the proposed algorithm is observed to provide a promising trade-off between the critical SKG performance metrics namely the Key Generation Rate (KGR), Bit Mismatch Probability (BMP), and the Key Randomness Property (KRP). Furthermore, a comparative performance analysis of the proposed algorithm with other notable techniques is also conducted to demonstrate the superior performance of the proposed algorithm.