Physical layer security is a promising technology for non-terrestrial wireless networks due to the lack of infrastructure over the low-altitude and high-altitude uncrewed aerial vehicle platforms. Physical layer security can be addressed using key-based and keyless approaches, such as channel adaptation and channel coding. In the key-based approach, the legitimate users generate secret keys by exploiting the unique random channel gains, which can be further used for information encryption and decryption. In non-terrestrial networks, the physical layer exploitation and the matching of generated secret keys on legitimate ends become more complex due to the 3-dimensional channel models, and the high mobility of uncrewed aerial vehicles, resulting in nonzero key. To address this challenge, this paper proposes an artificial intelligence-based key exchange mechanism where the secret keys are generated on the legitimate nodes of the communications links. The transmitter is responsible for training the neural network and transmitting the final synapses to the receiver to reconcile the information. The results are presented in terms of secret key waveforms. The success rate against the number of errors that occur in a single 128-bit key and 256-bit key shows a significant improvement.
Physical layer security is a promising technology for non-terrestrial wireless networks due to the lack of infrastructure over the low-altitude and high-altitude uncrewed aerial vehicle platforms. Physical layer security can be addressed using key-based and keyless approaches, such as channel adaptation and channel coding. In the key-based approach, the legitimate users generate secret keys by exploiting the unique random channel gains, which can be further used for information encryption and decryption. In non-terrestrial networks, the physical layer exploitation and the matching of generated secret keys on legitimate ends become more complex due to the 3-dimensional channel models, and the high mobility of uncrewed aerial vehicles, resulting in nonzero key. To address this challenge, this paper proposes an artificial intelligence-based key exchange mechanism where the secret keys are generated on the legitimate nodes of the communications links. The transmitter is responsible for training the neural network and transmitting the final synapses to the receiver to reconcile the information. The results are presented in terms of secret key waveforms. The success rate against the number of errors that occur in a single 128-bit key and 256-bit key shows a significant improvement.
Physical layer security is a promising technology for non-terrestrial wireless networks due to the lack of infrastructure over the low-altitude and high-altitude uncrewed aerial vehicle platforms. Physical layer security can be addressed using key-based and keyless approaches, such as channel adaptation and channel coding. In the key-based approach, the legitimate users generate secret keys by exploiting the unique random channel gains, which can be further used for information encryption and decryption. In non-terrestrial networks, the physical layer exploitation and the matching of generated secret keys on legitimate ends become more complex due to the 3-dimensional channel models, and the high mobility of uncrewed aerial vehicles, resulting in nonzero key. To address this challenge, this paper proposes an artificial intelligence-based key exchange mechanism where the secret keys are generated on the legitimate nodes of the communications links. The transmitter is responsible for training the neural network and transmitting the final synapses to the receiver to reconcile the information. The results are presented in terms of secret key waveforms. The success rate against the number of errors that occur in a single 128-bit key and 256-bit key shows a significant improvement.
5G wireless networks are evolving to meet the rapid increase in the demand of the users to get high-quality broadband services with high-speed data rate. This advance development came up with a lot of challenges such as coverage region, security, energy efficiency, spectrum utilization, cost, latency, and data rate. To ensure the security, there are many conventional key exchange algorithms that need complex infrastructure. In recent times, physical layer-based security has gained a lot of attention since it offers the simple and efficient way of key exchange by exploiting wireless channel characteristics. In this project, for secret key sharing between the two legitimate users through the correlated channel that is shared with eavesdropper, a pre-equalization transmit filter that inverts the main channel is employed. Further to decrease the probability of interception by eavesdropper while permitting successful decoding by the legitimate user. Low-Density Parity Check (LDPC) code is used to assure reliable communication. Modulation schemes such as basic QPSK and 64,128 and 256- QAM are used to achieve higher bit rates. To achieve high secrecy between the legitimate users zero forcing algorithm is used to invert the channel. Through simulations, the effect of channel correlation will be studied to analyze the low secret key mismatch between the Bob and Eve. Here Eve having high Bit Error Rate(BER) so that eve could not obtain the original bits. The simulation will be done using Matlab.
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.
In the rapidly evolving landscape of wireless communication, securing transmitted information remains a critical challenge. This study provides a comprehensive review of secret key generation (SKG) techniques designed to secure physical layer wireless communication channels. We focus on key generation methods that exploit physical layer characteristics such as channel state information, received signal strength, and bit error rate. The computational complexity of these techniques is also examined, emphasizing the trade-offs between security and efficiency for real-world implementations. The review identifies open challenges such as developing low-cost, resource-efficient systems, managing artificial noise, and addressing the impact of mobility and multiuser massive MIMO systems on SKG rates. Our findings aim to guide the development of more efficient, scalable SKG methods for next-generation wireless networks. Additionally, we propose a method for securely generating identical secret keys between nodes using Cuckoo filters and a Map-Reduce model for frame hashing, ensuring long-term security in IoT environments.
Physical layer security (PLS) can be used to pro-vide keyless and innately secure communications by exploiting the propagation characteristics of the wireless channel at the physical layer (PHY) layer to co-generate cipher keys for symmetric encryption. While existing methods use channel state information as input for secret key generation, we consider a novel approach that derives features from frequency-time domain representation of in-phase and quadrature (IQ) samples to derive initial keys with high agreement rate. A recurrent neural network (RNN) with a quadruplet loss function is trained based on spectrograms collected from bi-directional time-division duplex channel probing between the two parties and on spectrograms collected from a passive eavesdropper. Information reconciliation is achieved using Reed Solomon codes. At the last step of the protocol, a 512-bit cryptographic key is generated by feeding the reconciled bit sequence to the Secure Hash Algorithm 3. Our experiments with software-defined radios in both indoor and outdoor environments evaluate the proposed key generation system under different channel propagation environments and hardware inaccuracies. We demonstrate that the proposed RNN-assisted channel feature extractor trained on indoor channel probe data operates successfully in two outdoor setups and produces up to 70 amplified keys out of 100 probes exchanged between two trusted parties i.e., Alice and Bob, while the passive eavesdropper fails to directly predict any of the generated keys.
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.