The physical attributes of the dynamic vehicle-to-vehicle propagation channel can be utilized for the generation of highly random and symmetric cryptographic keys. However, in a physical-layer key agreement scheme, nonreciprocity due to inherent channel noise and hardware impairments can propagate bit disagreements. This has to be addressed prior to the symmetric key generation which is inherently important in Social Internet of Things networks, including in adversarial settings (e.g., battlefields). In this paper, we parametrically incorporate temporal variability attributes, such as 3-D scattering and scatterers' mobility. Accordingly, this is the first work to incorporate such features into the key generation process by combining nonreciprocity compensation with turbo codes (TCs). Preliminary results indicate a significant improvement when using TCs in bit mismatch rate and key generation rate in comparison to sample indexing techniques.
The physical attributes of the dynamic vehicle-to-vehicle propagation channel can be utilized for the generation of highly random and symmetric cryptographic keys. However, in a physical-layer key agreement scheme, nonreciprocity due to inherent channel noise and hardware impairments can propagate bit disagreements. This has to be addressed prior to the symmetric key generation which is inherently important in Social Internet of Things networks, including in adversarial settings (e.g., battlefields). In this paper, we parametrically incorporate temporal variability attributes, such as 3-D scattering and scatterers' mobility. Accordingly, this is the first work to incorporate such features into the key generation process by combining nonreciprocity compensation with turbo codes (TCs). Preliminary results indicate a significant improvement when using TCs in bit mismatch rate and key generation rate in comparison to sample indexing techniques.
In intelligent transportation systems (ITS), communications between vehicles, i.e. vehicle-to-vehicle (V2V) communications are of greatest importance to facilitate autonomous driving. The current state-of-the-art for secure data exchange in V2V communications relies on public-key cryptography (PKC) consuming significant computational and energy resources for the encryption/decryption process and large bandwidth for the key distribution. To overcome these limitations, physical-layer security (PLS) has emerged as a lightweight solution by exploiting the physical characteristics of the V2V communication channel to generate symmetric cryptographic keys. Currently, key-generation algorithms are designed via empirical parameter settings, without resulting in optimum key-generation performance. In this paper, we devise a key-generation algorithm for PLS in V2V communications by introducing a novel channel response quantisation method that results in optimum performance via analytical parameter settings. Contrary to the current state-of-the-art, the channel responses incorporate all V2V channel attributes that contribute to temporal variability, such as three dimensional (3D) scattering and scatterers' mobility. An extra functionality, namely, Perturbe-Observe (PO), is further incorporated that enables the algorithm to adapt to the inherent non-reciprocity of the V2V channel responses at the legitimate entities. Optimum performance is evidenced via maximisation of the key bit generation rate (BGR) and key entropy (H) and minimisation of the key bit mismatch rate (BMR). A new metric is further introduced, the so-called secret-bit generation rate (SBGR), as the ratio of the number of bits which are successfully used to compose keys to the total amount of channel samples. SBGR unifies BGR and BMR and is thus maximised by the proposed algorithmic process.
Internet-of-Things (IoT) networks generally contain resource-constrained devices that require an energy-efficient key generation procedure to producing secure keys at a faster rate. The physical characteristics of the wireless channel can be exploited to secure communication within IoT networks. In particular, secret keys can be generated by leveraging on the randomness of the wireless physical channel between two communicating parties. The conventional mechanism of generating keys at the physical layer, i.e., using channel probing, quantization, information reconciliation, and privacy amplification, may not be preferable for IoT devices. In addition, in some cases IoT devices may be deployed in static environments, wherein the channel coherence time is too high to generate keys at a faster rate and with the desired randomness. This study proposes a mapping table-based key distribution scheme for IoT environments, wherein multiple characteristics of the random channel are combined to improve not only the key generation rate (KGR) but also the key agreement rate (KAR) and bit error rate (BER). In the proposed scheme, both the channel magnitude and the phase are exploited in the key generation process. The proposed scheme is immune to channel estimation errors while providing sufficient randomness in the static environment. Additionally, the scheme is thoroughly investigated for different scenarios including the case of a smarter eavesdropper, which attempts to estimate the channel between the legitimate nodes. This case verifies the robustness of the proposed scheme in different settings and attack models.
In intelligent transportation systems (ITS), communications
between vehicles, i.e. vehicle-to-vehicle (VV) communications
are of greatest importance to facilitate autonomous
driving. The current state-of-the-art for secure data exchange
in VV communications relies on public-key cryptography (PKC)
consuming significant computational and energy resources for the
encryption/decryption process and large bandwidth for the key
distribution. To overcome these limitations, physical-layer security
(PLS) has emerged as a lightweight solution by exploiting the
physical characteristics of the VV communication channel to generate
symmetric cryptographic keys. Currently, key-generation
algorithms are designed via empirical parameter settings, without
resulting optimum key-generation performance. In this paper,
we devise a key-generation algorithm, namely, Perturbe-Observe
(PO), for PLS in VV communications by introducing a novel
channel response quantisation method that results optimum
performance via analytical parameter settings. Contrary to the
current state-of-the-art, the channel responses incorporate all VV
channel attributes that contribute to temporal variability, such
as three dimensional (3D) scattering and scatterers’ mobility. An
extra add-on is further incorporated that enables the algorithm
to adapt to the inherent non-reciprocity of the VV channel
responses at the legitimate entities. Optimum performance is
evidenced via maximisation of the key bit generation rate (BGR)
and key entropy (H) and minimisation of the key bit mismatch
rate (BMR). A new metric is further introduced, the so-called
secret-bit generation rate (SBGR), as the ratio of the number
of bits which are successfully used to compose keys to the total
amount of channel samples. SBGR unifies BGR and BMR and
is thus maximised by the proposed algorithmic process.
Vehicular communication is delay sensitive and demands high security. However, the time for key establishment between node pairs is limited due to the unique features of fast-moving and high dynamics in Vehicular ad hoc networks (VANETs). Moreover, under high-mobility scenarios, the communication performance of Orthogonal Frequency Division Multiplexing (OFDM) signals degrades significantly, while Orthogonal Time Frequency Space (OTFS) signals maintains better performance. Towards this end, considering the time for key establishment, we propose an efficient physical layer secret key generation (PLSKG) scheme utilizing OTFS channel state information (CSI). First, we preprocess the estimated CSI with a moving average coding (MAC) algorithm to improve channel reciprocity. Next, we adopt a bidirectional difference quantization (BDQ) for increased key bit generation and propose a one-way reconciliation protocol to remove bit mismatch. Results from extensive simulations have verified that our scheme achieves random 0-b disagreement keys with a high key generation rate and reduced time for key establishment compared to other schemes.
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.
Physical Layer Secret Key Generation (PSKG) exploits wireless channel reciprocity to establish cryptographic keys between legitimate users, providing a promising solution for securing IoT networks. However, reciprocity in time-division duplex (TDD) systems is often compromised by hardware imperfections and channel noise, posing significant challenges for reliable key generation. To address these issues, this paper proposes an autoencoder-based noise augmentation (AE-NA) model that operates in an unsupervised manner. Specifically, the proposed model integrates dynamic Gaussian noise augmentation directly into the autoencoder architecture. This approach enhances feature extraction capabilities and improves noise robustness, enabling the network to learn noise-invariant representations effectively from noisy channel estimates. Furthermore, we present a comprehensive PSKG framework that incorporates the proposed AE-NA model. Reported results demonstrate that the AE-NA-based PSKG significantly reduces the key disagreement ratio (KDR) while enhancing key randomness, thereby providing a scalable and effective solution for secure key generation in dynamic IoT environments.