Digital healthcare services are rapidly evolving for new methodologies, including hospital-to-home (H2H) services and intelligent Internet-of-Medical-Things (IIoMT). The sixth generation (6G) technology is considered as the fabric that facilitates the realization of these technologies, creating a paradigm shift towards personalized e-health services. To deal with the high security requirements and the energy constraints of 6G-enabled e-health services, we propose a lightweight learning-based key generation scheme for a pair of wireless-powered nodes in a cooperative communication system, where the legitimate nodes and the intermediate node have low-cost hardware-impaired transceivers. We utilize an echo state network (ESN) to enhance the “randomness distillation” phase, in which the legitimate parties try to obtain a common source of randomness as the raw data for key agreement. The PHY-based observed data is passed to the ESN, containing a reservoir of sparsely connected neurons to compensate for observation mismatches caused by the unbalanced hardware impairments. The output of the ESN can then be utilized to extract the secret key between e-health endpoints. Numerical experiments verify the performance gain of our proposed echo-based approach, resulting in 50% less required inference time compared with a fully-connected neural network (FCNN). Moreover, a performance gain of about 32% in terms of mean-square error (MSE) is achieved compared with a conventional PHY-only scheme.
Digital healthcare services are rapidly evolving for new methodologies, including hospital-to-home (H2H) services and intelligent Internet-of-Medical-Things (IIoMT). The sixth generation (6G) technology is considered as the fabric that facilitates the realization of these technologies, creating a paradigm shift towards personalized e-health services. To deal with the high security requirements and the energy constraints of 6G-enabled e-health services, we propose a lightweight learning-based key generation scheme for a pair of wireless-powered nodes in a cooperative communication system, where the legitimate nodes and the intermediate node have low-cost hardware-impaired transceivers. We utilize an echo state network (ESN) to enhance the “randomness distillation” phase, in which the legitimate parties try to obtain a common source of randomness as the raw data for key agreement. The PHY-based observed data is passed to the ESN, containing a reservoir of sparsely connected neurons to compensate for observation mismatches caused by the unbalanced hardware impairments. The output of the ESN can then be utilized to extract the secret key between e-health endpoints. Numerical experiments verify the performance gain of our proposed echo-based approach, resulting in 50% less required inference time compared with a fully-connected neural network (FCNN). Moreover, a performance gain of about 32% in terms of mean-square error (MSE) is achieved compared with a conventional PHY-only scheme.
Wireless secret key generation (WSKG) facilitates efficient key agreement protocols for securing the sixth generation (6G) wireless networks thanks to its inherently lightweight functionality. Nevertheless, with the existence of adversarial attacks or internal impairments, WSKG can be negatively affected during the randomness distillation, where the legitimate parties measure a source of common randomness. In this paper, we propose a learning-aided approach for cooperative WSKG under man-in-the-middle (MitM) adversarial attack, while the legitimate nodes suffer fromhardware impairments(HIs). The key idea is to process the PHY-attribute data on the application layer via deploying a deep neural network (DNN) to enhance the randomness distillation. This way, we realize a learning-based software-centric security solution. More specifically, we take into account the sequence-type nature of observed data, and propose a DNN comprised ofgated recurrent units(GRUs) to learn the sequence of observations at legitimate endpoints, while the MitM is also alleviated. Our numerical results verify the performance gain of the proposed learning-based approach compared with the state-of-the-arts. Moreover, time and computation complexity of different learning-based models are studied to address the complexity-performance trade-off. Our tests highlight a performance gain of about 43% in terms of mean-square error (MSE) in comparison with a conventional PHY-only scheme.
Wireless secret key generation (WSKG) allows efficient key agreement protocols for securing the sixth generation (6G) wireless networks. Nevertheless, due to external adversaries or internal impairments, WSKG schemes might become vulner-able during the randomness distillation, where the legitimate nodes try to observe their source of common randomness. In this paper, we investigate the WSKG scheme with legitimate parties suffering from hardware impairments (HIs), while an active adversary acts as a man-in-the-middle (MiM) via injecting fake pilot signals. We first utilize randomized pilots to overcome the MiM. We also leverage the concept of recurrent neural networks (RNNs) to further enhance the randomness distillation. More specifically, the long short-term memory networks (LSTMs)-as a well-established type of RNNs-are implemented to learn the long-term dependencies between the observations of legitimate parties. The achievable secret key rate (SKR) and the impact of MiM on system's performance are analyzed. Our numerical results verify the performance gain of our proposed learning-based approach compared with the state-of-the-art methods and provide useful insights on system design. We show that our RNN-based approach achieves 30% and 15% improvement in terms of observation mismatches compared with the naïve scheme and the fully-connected benchmarks, respectively.
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.
Reliable communication over Wireless Body Area Networks (WBANs) is an essential aspect of Wireless Body Area Networks (WBANs) because the physiological data flows between wearable sensor nodes is sensitive data. The convectional cryptography methods usually apply a lot of computational complexity as well as energy overhead besides which they are not going to fit in the ultra-low-power WBANs. In the current paper a light-weight, energy-efficient Physical Layer Key Generation (PLKG) scheme the possibilities of which based on the native randomness and reciprocity of the wireless channel is used to generate secure symmetric keys is presented. With WBANs, there is a lot of temporal and spatial dynamics in the human body and its surrounding environment, which causes great variability in channel characteristics, in terms of Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). Such physical layer parameters can be used as perfect entropy sources in generation of unique, unpredictable secret keys with whose knowledge, communication between nodes is possible and mutually exclusive only. The channel probing in the proposed framework is also robust, multi-bit quantization is performed adaptively, information reconciliation is performed through error correction codes, and privacy amplification is made through cryptographic hashing. We have developed a resilient system against bit mismatch and efficiently against passively eavesdropping, we guarantee the secrecy of the generated keys under adversary situation. With realistic WBAN models simulation and experiment validation shows that the proposed approach is capable of generating keystokes with very high key generation rate (50 bps) and a bit mismatch rate of lower than 3 percent as compared to existing schemes that solely relied on RSSI, making this protocol more secure and reliable to use. In addition, the framework meets the statistical randomness requirements specified by NIST and it does not leak mutual information to eavesdroppers. The scheme allows real-time use on resource-constrained devices, e.g., wearable medical devices and health-monitoring sensor, as it has low computational and memory overhead. The findings establish the feasibility of employing physical layer properties to achieve secure WBANs communication without any pre-shared keys or a centralized trust paradigm, thus, opening the way to scalable and privacy-preserving healthcare systems.
With a massive amount of wireless sensor nodes in Internet of Things (IoT), it is difficult to establish key distribution and management mechanism for traditional encryption technology. Alternatively, the physical layer key generation technology is promising to implement in IoT, since it is based on the principle of information-theoretical security and has the advantage of low complexity. Most existing key generation schemes assume that eavesdropping channels are independent of legitimate channels, which may not be practical especially when eavesdropper nodes are near to legitimate nodes. However, this paper investigates key generation problems for a multi-relay wireless network in IoT, where the correlation between eavesdropping and legitimate channels are considered. Key generation schemes are proposed for both non-colluding and partially colluding eavesdroppers situations. The main idea is to divide the key agreement process into three phases: 1) we first generate a secret key by exploiting the difference between the random channels associated with each relay node and the eavesdropping channels; 2) another key is generated by integrating the residual common randomness associated with each relay pair; 3) the two keys generated in the first two phases are concatenated into the final key. The secrecy key performance of the proposed key generation schemes is also derived with closed-forms.
The convergence of Sixth-Generation (6G) wireless networks and the Internet of Things (IoT) demands unprecedented levels of performance, scalability, and security. Traditional architectures are increasingly inadequate in addressing the computational and security challenges posed by massive IoT connectivity, ultra-low latency, and high data throughput. This paper proposes a novel quantum-assisted architecture that integrates quantum computing and quantum communication principles to enhance the efficiency and security of 6G-enabled IoT systems. The framework leverages quantum key distribution (QKD), quantum machine learning (QML), and entanglement-assisted routing to provide end-to-end encryption, intelligent resource allocation, and resilient data transmission. Our simulation results and comparative analysis demonstrate significant improvements in network throughput, latency, and security resilience compared to classical 6G-IoT architectures. This research establishes a foundational step toward realising secure and intelligent next-generation wireless networks through the integration of quantum technology.