Key Physical Layer Research Articles

Performance Analysis of Physical Layer Key Generation Relying on Discrete Random Phase Injection

Performance Analysis of Physical Layer Key Generation Relying on Discrete Random Phase Injection

Enhanced Message Authentication Encryption Scheme Based on Physical-Layer Key Generation in Resource-Limited Internet of Things

Enhanced Message Authentication Encryption Scheme Based on Physical-Layer Key Generation in Resource-Limited Internet of Things

Artificial Intelligence-Assisted RFID Tag-Integrated Multi-Sensor for Quality Assessment and Sensing.

Radio frequency identification (RFID) is well known as an identification, track, and trace approach and is considered to be the key physical layer technology for the industrial internet of things (IIoT). However, IIoT systems have to introduce additional complex sensor networks for pervasive monitoring, and there are still challenges related to item-level sensing and data recording. To overcome the shortage, this work proposes an artificial intelligence (AI)-assisted RFID-based multi-sensing technology. Both passive and semi-passive RFID tag-integrated multi-sensors are developed. The main contributions and the novelty of this investigation are as follows. A UHF RFID tag-integrated multi-sensor with a boosted charge pump is proposed; it enables high RF signal sensitivity and a long operational range. The whole hardware design, including the antenna and energy harvester, are studied. Moreover, a demonstration with real-world ham product sensing data is conducted. This work also proposes and successfully demonstrates the integration of machine learning algorithms, specifically the NARX neural network, with RFID sensing data for food product quality assessment and sensing (QAS). This application of machine learning to RFID-generated data for quality assessment is also a novel aspect of the research. The deployment of an autoregressive model with an exogenous input (NARX) neural network model, tailored for nonlinear processes, emerges as the most effective, achieving a root mean square error (RMSE) of 0.007 and an R-squared value of 0.99 for ham product QAS. By deploying the technology, low-cost, timely, and flexible product QAS can be achieved in manufacturing industries, which helps product quality improvement and the optimization of the manufacturing line and supply chain.

Intelligent Reflective Surface Aided Channel Phase Probing for Physical Layer Key Generation in Transportation Systems

Intelligent Reflective Surface Aided Channel Phase Probing for Physical Layer Key Generation in Transportation Systems

A Reliable Physical Layer Key Generation Scheme Based on RSS and LSTM Network in VANET

With the rapid development of information and communication technology, Vehicular AD hoc Networks (VANET) has attracted more and more attention. In order to provide traffic safety services, vehicles frequently share information related to road traffic, such as vehicle motion data and traffic flow data. Reliable key generation is the basis of VANET security system construction. At present, most key generation schemes rely on a trusted third-party, so there are security risks. Traditional key agreement protocols have high overhead, and is not suitable for the latency-sensitive requirements of VANET. The physical layer security technology extracts the fingerprint of the wireless channel and the identity of the device, and generates the key quickly and in real time without the third party distribution. A physical layer key generation scheme based on Received Signal Strength (RSS) is proposed to realize Vehicle-to-Vehicle (V2V) secure communications. Firstly, a network model based on Long Short-Term Memory (LSTM) network and Kalman Filtering is proposed to effectively enhance the reciprocity of physical layer information in dynamic environment. Secondly, a lossless quantization scheme is proposed, which achieves a lower bit disagreement rate and a higher bit generation rate. Thirdly, the inconsistent bits are corrected by fuzzy extractor, the confidentiality of the key exchange process is enhanced by zero knowledge proof, and the security of the key is improved by using hash function for privacy amplification. Finally, the experimental results show that the proposed scheme has great improvements in data correlation, bit disagreement rate, bit generation rate and bit distribution randomness.

AI-Based Physical Layer Key Generation in Wireless Communications: Current Advances, Open Challenges, and Future Directions

AI-Based Physical Layer Key Generation in Wireless Communications: Current Advances, Open Challenges, and Future Directions

RIS-Aided Physical Layer Key Generation of IoT Network in Static Environments (March 2023)

RIS-Aided Physical Layer Key Generation of IoT Network in Static Environments (March 2023)

A Countermeasure Against RIS Jamming Attack in Physical-Layer Key Generation

A Countermeasure Against RIS Jamming Attack in Physical-Layer Key Generation

A Novel Hybrid Protocol and Code Related Information Reconciliation Scheme for Physical Layer Secret Key Generation

Wireless networks are vulnerable to various attacks due to their open nature, making them susceptible to eavesdropping and other security threats. Eavesdropping attack takes place at the physical layer. Traditional wireless network security relies on cryptographic techniques to secure data transmissions. However, these techniques may not be suitable for all scenarios, especially in resource-constrained environments such as wireless sensor networks and adhoc networks. In these networks having limited power resources, generating cryptographic keys between mobile entities can be challenging. Also, the cryptographic keys are computationally complex and require key management infrastructure. Physical Layer Key Generation (PLKG) is an emerging solution to address these challenges. It establishes secure communication between two users by taking advantage of the wireless channel's inherent features. PLKG process involves channel probing, quantization, information reconciliation (IR) and privacy amplification to generate symmetric secret key. The researchers have used various PLKG techniques to get the secret key, sTop of Form
 till they face problems in the IR scheme to obtain symmetric keys between the users who share the same channel for communication. Both the code based and protocol based methods proposed in the literature have advantages and limitations related to their performance parameters such as information leakage, interaction delay and computation complexity. This research work proposes a novel IR mechanism that combines the protocol and code-based error correction methods to obtain reduced Bit Mismatch Rate (BMR), reduced information leakage, reduced interaction delay, and reduced computational time to enhance physical layer secret key's quality. In the proposed research work, the channel samples are generated using the Received Signal Strength (RSS) and Channel Impulse Response (CIR) parameters. These samples are quantized using Vector Quantization with Affinity Propagation Clustering (VQAPC) method to generate the preliminary key. The samples collected by the two users who wish to communicate, (for example Alice and Bob) will be different due to noise in the channel and hardware limitations. Hence their preliminary keys will be different. Removing this discrepancy between Bob's and Alice's initial keys, using novel Hybrid Protocol and Code related Information Reconciliation (HPC-IR) scheme to generate error corrected key, is the most important contribution of this research work. This key is further encoded by the MD5 hash function to generate a final secret key for exchanging information between two users over the wireless channel. It is observed that the proposed HPC-IR scheme achieves BMR of 19.4%, information leakage is 0.002, interaction delay is 0.001 seconds and computation time is 0.02 seconds.

A Highly Consistent and High-speed Physical Layer Key Generation Scheme

A Highly Consistent and High-speed Physical Layer Key Generation Scheme

Physical Layer Key Generation Scheme for MIMO System Based on Feature Fusion Autoencoder

Recently, the use of wireless channel state information (CSI) to generate encryption keys in the physical layer has gained significant attention from researchers. Unlike classical cryptography, this approach relies on the variability of the wireless channel, channel reciprocity, and spatial decorrelation to ensure security, making it more lightweight and providing strong randomness. This paper proposes a physical layer key generation scheme for wireless LAN MIMO systems based on feature fusion autoencoder (FFAEncoder) to address the issue of a high key disagreement rate (KDR). Our approach involves extracting amplitude and phase features separately, fusing them through multiplication operator in a neural network, and using an autoencoder to extract common features. The proposed scheme was evaluated on multiple datasets in different real-world scenarios, and it was found that the transmitter and receiver codeword’s mean squared error (MSE) and mean absolute error (MAE) were smaller than those of the current models, indicating better key generation performance. Additionally, the proposed scheme’s KDR was smaller, with a decay rate faster than that of the other two models in the same environment, and the primary key bits were 1/2 and 1/3 that of the other models, respectively, as the signal-to-noise ratio (SNR) increased.

Lightweight and Fast Physical-Layer Key Generation in FDD Systems using Random Forest

Physical-layer Secure Key Generation (SKG) in wireless communication provides high-level security originating from channel uniqueness. However, the carrier frequency difference in Frequency Division Duplexing (FDD) systems degrades the channel reciprocity, compromising the feasibility of SKG and subsequently the Key Generation Rate (KGR). We propose a lightweight and fast SKG scheme with enhanced KGR in FDD systems, using the Random Forest (RF) algorithm in conjunction with a Multi-to-Single Mapping (M2SM) strategy. Besides, the Moving Average (MA) and subtraction algorithms further enhance the SKG performances under noisy and frequency-selective fading FDD channels. Experimental results revealed an error-free KGR of 57.71 Kbps, with superior randomness, showing the practicability of high-speed and robust SKG in FDD systems. Moreover, the proposed scheme has low complexity, high flexibility, and information-theoretic security.

Physical layer security of single‐input multiple‐out relay networks over Beaulieu‐Xie fading channels

AbstractWith the progress of communication technology, the increasing wireless devices are hooked up the networks, and the consequent security problems cannot be ignored. At present, the solutions mainly include secret key system and physical layer security (PLS). However, secret key system that relies on highly complex algorithms for encryption and decryption still has the risk of being cracked by powerful computers. In this paper, the PLS of Beaulieu‐Xie fading channels is studied. To improve the transmission quality and support higher speed data transmission, the single‐input multiple‐out (SIMO) system considered is the Wyner eavesdropper model with decoding and forwarding (DF) relay over Beaulieu‐Xie fading. The closed‐form theoretical formulas for secure outage probability (SOP) and strictly positive secrecy capacity (SPSC) are obtained by complex unified functional representation, which includes two cases: and . Moreover, the theoretical formulas are verified by Monte Carlo results. By comparing the simulation results with the theoretical analysis, the authors discussed the factors to improve the secure performance. Experiments show that the larger ratio of , the smaller ratio of , smaller signal‐to‐noise ratio (SNR) of eavesdropping channels, smaller threshold and more legitimate antennas are beneficial to the improvement of anti‐interference capability for the relay networks with Beaulieu‐Xie distributions when the legal channel link R→D is in a state of high SNR.

A Physical Layer Key Generation Scheme Based on Deep Learning Compensation and Balanced Vector Quantization

Channel reciprocity is the foundation for physical layer key generation, which is influenced by noise, hardware impairments, and synchronization offsets. Weak channel reciprocity will result in a high key disagreement rate (KDR). The existing solutions for improving channel reciprocity cannot achieve satisfactory performance improvements. Furthermore, the existing quantization algorithms generally use one-dimensional channel features to quantize and generate secret keys, which cannot fully utilize channel information. The multidimensional vector quantization technique also needs to improve in terms of randomness and time complexity. This paper proposes a physical layer key generation scheme based on deep learning and balanced vector quantization. Specifically, we build a channel reciprocity compensation network (CRCNet) to learn the mapping relationship between Alice and Bob’s channel measurements. Alice compensates for channel measurements via a trained CRCNet to reduce channel measurement errors between legitimate users and enhance channel reciprocity. We also propose a balanced vector quantization algorithm based on integer linear programming (ILP-BVQ). ILP-BVQ reduces the time complexity of quantization on the basis of ensuring key randomness and a low KDR. Simulation results showed that the proposed CRCNet performs better in terms of channel reciprocity and KDR, while the proposed ILP-BVQ algorithm improves time consumption and key randomness.

Formally verifying security protocols built on watermarking and jamming

Physical layer security mechanisms use primitives that exploit physical properties of the communication channel to protect data. Protecting communications at the physical layer offers some advantages, e.g., in terms of reduced computations, since complex cryptographic procedures are not executed, However, these mechanisms lack a formal specification that prevent protocols and applications that use them from being verified and compared with those based on cyptography. Here we start filling this gap by providing an axiomatization of key physical layer security primitives and proposing a variant of the Dolev–Yao attacker model that takes them into account. We show that our formalization enables applying existing automatic tools for verifying security of protocols. Then, we show that these primitives are a valuable alternative and effective complement to cryptography, because they ensure confidentiality and integrity but require a lower energy consumption and often they also reduce transmission time. Finally, we characterize the specific application domains and network features that make adopting these security mechanisms particularly profitable with respect to the AES cypher.

Physical Layer Key Generation Method Based on SVD Pre-processing

Environmental factors such as channel noise and hardware fingerprints affect the encryption effect of physical layer key generation techniques, resulting in low consistency of generated keys. Feature pre-processing is a common means of improving consistency of keys. However, most of the existing feature pre-processing algorithms improve key consistency by sacrificing key generation rate, which is not very usable. Therefore, it is proposed a physical layer key generation method based on SVD pre-processing. This method uses the SVD feature processing algorithm to pre-process the channel features extracted from both sides of the communication before quantization, in order to simultaneously improve key consistency and key generation rate. The simulation results show that when the channel SNR is greater than 10 dB, the BER of the SVD scheme is significantly lower compared to the scheme without pre-processing and the DCT and PCA pre-processing schemes; when the SNR is greater than 20 dB, the SVD scheme KGR can reach a level of 10bit/s, which is significantly higher than the other three schemes. The results show that this scheme can significantly increase the key generation rate while effectively improving key consistency.

Security Analysis of Triangle Channel-Based Physical Layer Key Generation in Wireless Backscatter Communications

Ambient backscatter communication (AmBC) enables ultra-low-power communications by backscattering ambient radio frequency (RF) signals and harvesting energy simultaneously. It has emerged as a cutting-edge technology for supporting a variety of Internet of Things (IoT) applications. However, existing research lacks effective secret key sharing schemes for safeguarding communications between resource-constrained backscatter devices (BDs) in AmBC systems. In this paper, we present, Tri-Channel, a novel physical layer key generation scheme between two BDs by multiplying downlink signals and backscatter signals to obtain the information of a triangle channel as a shared random secret source for key generation. In particular, we analyze the security of our scheme under both passive and active attacks, concretely Eavesdropping Attack (EA), Control Channel Attack (CCA), Signal Manipulative Attack (SMA), and Untrusted RF-Source Attack (URSA). Through theoretical analysis and simulations by comparing with a traditional scheme (named Tradi-Channel), we found that our scheme consistently outperforms the Tradi-Channel under the EA and two active attacks (CCA and SMA). In addition, it shows better security performance under URSA, which is proposed based on the unauthenticated characteristic of BDs in Tri-Channel, even though URSA is more vital than SMA. Concretely, Tri-Channel’s secret key rate (SKR) outperforms Tradi-Channel’s under the above four passive and active attacks. This implies that our scheme is advanced in terms of both security and efficiency of key generation. Numerous extensive simulations further prove our theoretical analysis results.

Intelligent Reflecting Surface-Assisted Physical Layer Key Generation with Deep Learning in MIMO Systems

Physical layer secret key generation (PLKG) is a promising technology for establishing effective secret keys. Current works for PLKG mostly study key generation schemes in ideal communication environments with little or even no signal interference. In terms of this issue, exploiting the reconfigurable intelligent reflecting surface (IRS) to assist PLKG has caused an increasing interest. Most IRS-assisted PLKG schemes focus on the single-input-single-output (SISO), which is limited in future communications with multi-input-multi-output (MIMO). However, MIMO could bring a serious overhead of channel reciprocity extraction. To fill the gap, this paper proposes a novel low-overhead IRS-assisted PLKG scheme with deep learning in the MIMO communications environments. We first combine the direct channel and the reflecting channel established by the IRS to construct the channel response function, and we propose a theoretically optimal interaction matrix to approach the optimal achievable rate. Then we design a channel reciprocity-learning neural network with an IRS introduced (IRS-CRNet), which is exploited to extract the channel reciprocity in time division duplexing (TDD) systems. Moreover, a PLKG scheme based on the IRS-CRNet is proposed. Final simulation results verify the performance of the PLKG scheme based on the IRS-CRNet in terms of key generation rate, key error rate and randomness.

Reconfigurable Intelligent Surface for Physical Layer Key Generation: Constructive or Destructive?

Physical layer key generation (PKG) is a promising means to provide on-the-fly shared secret keys by exploiting the intrinsic randomness of the radio channel. However, the performance of PKG is highly dependent on the propagation environments. Due to its feature of controlling the wireless environment, reconfigurable intelligent surface (RIS) is appealing to be applied in PKG. In this article, in contrast to the existing literature, we investigate both the constructive and destructive effects of RIS on the PKG scheme. For the constructive aspect, we have identified static and wave-blockage environments as two RIS-empowered-PKG applications in future wireless systems. In particular, our experimental results in a static environment showed that RIS can enhance the entropy of the secret key, achieving a key generation rate (KGR) of 97.39 bit/s with a bit disagreement rate (BDR) of 0.083. In multi-user systems where some remote users are in worse channel conditions, the proposed RIS-assisted PKG algorithm improves the sum secret key rate by more than 2 dB, compared to the literature. Furthermore, we point out that RIS could be utilized by an attacker to perform new jamming and leakage attacks and give countermeasures, respectively. Finally, we outline future research directions for PKG systems in light of the RIS.

A Physical-Layer Key Generation Approach Based on Received Signal Strength in Smart Homes

Due to the characteristics of wireless network transmission, smart home devices are vulnerable to malicious attacks. Malicious attackers can not only intercept the transmission data of smart home devices to grasp the user’s personal privacy information but also can eavesdrop the transmission between devices to forge the user’s legal information. These attacks have brought great security risks to people’s daily lives. In order to ensure the security of transmitted data, establishing keys for smart home devices is necessary. In this article, an adaptive physical-layer key generation scheme based on received signal strength (RSS) is proposed in Smart Homes. The scheme performs group quantization and adaptive quantization on the collected RSS measurements. The design of group quantization improves the randomness of the generated keys and makes 0 and 1 in generated keys more evenly distributed. The advantage of adaptive quantization is to design adaptive quantization intervals. The smart home devices can select the appropriate quantized reference levels according to the RSS measurements in different scenarios. In order to verify the practicability of the key generation scheme, we analyze the performance of the proposed scheme in static and dynamic scenarios. In addition, this scheme is compared with other key generation schemes from various performance indicators. The comparison results show that our scheme is better than other schemes in terms of randomness.