Devices in the Internet of Things (IoT) are usually limited in computing resources and energy capacity, which means that encryption schemes with higher complexity are not suitable for them to ensure secure communication. As a promising solution to this problem, physical layer key generation suggests that shared secret keys can be generated from noisy wireless channel measurements to enhance the security of wireless communications. In this article, we propose a key generation scheme with extremely low implementation complexity, which allows physical layer key generation to be implemented on IoT nodes. First, we preprocess the channel measurements with simple moving average filtering before quantization to improve channel reciprocity. Next, a bidirectional difference quantization scheme is proposed to realize reliable quantization of channel measurements, which is ingenious in that the quantization process does not depend on quantization thresholds, and thus, the mismatched key bits caused by measurements close to quantization thresholds can be effectively avoided. Then, we propose an improved Cascade protocol to achieve lightweight and efficient information reconciliation. The simulation results show that our scheme can well balance the reliability and efficiency of key generation, and has excellent performance in terms of implementation complexity and key randomness.
Devices in the Internet of Things (IoT) are usually limited in computing resources and energy capacity, which means that encryption schemes with higher complexity are not suitable for them to ensure secure communication. As a promising solution to this problem, physical layer key generation suggests that shared secret keys can be generated from noisy wireless channel measurements to enhance the security of wireless communications. In this article, we propose a key generation scheme with extremely low implementation complexity, which allows physical layer key generation to be implemented on IoT nodes. First, we preprocess the channel measurements with simple moving average filtering before quantization to improve channel reciprocity. Next, a bidirectional difference quantization scheme is proposed to realize reliable quantization of channel measurements, which is ingenious in that the quantization process does not depend on quantization thresholds, and thus, the mismatched key bits caused by measurements close to quantization thresholds can be effectively avoided. Then, we propose an improved Cascade protocol to achieve lightweight and efficient information reconciliation. The simulation results show that our scheme can well balance the reliability and efficiency of key generation, and has excellent performance in terms of implementation complexity and key randomness.
Modern networking technologies involve the interconnection of smart and intelligent physical objects to provide advanced services. These physical objects may be part of various network technologies like WLAN, wireless sensor network (WSN), Internet of Things (IoT), etc., and share data in wireless mode, which should be secured from threats. These physical devices possess many constraints like power, computational complexity and storage, and are required to have lightweight security algorithms. Traditionally, security is achieved by the higher layers of the communication model, which has high computational complexity and consumes more power. In this chapter, a secure-key-generation scheme from physical layer characteristics is introduced as a possible lightweight security alternative to traditional upper-layer security approaches. The proposed scheme is based on the wiretap-channel model and security is achieved by generating keys at both communicating ends independently, based on inherent common-channel characteristics like randomness. It is mainly based on the principle of spatial decorrelation and channel reciprocity for identical carrier frequency. There is no provision for sharing keys as in public-key cryptography, so physical-layer key generation is not vulnerable to threats like key leakage. The generated key at the transmitter encrypts plain text; the generated cipher is transmitted on the common channel and is decrypted at the receiver end using the key generated by the receiver. Key generation consists of fundamental blocks like channel probing and quantization followed by information reconciliation and privacy amplification. The physical-layer key generation is already a power-efficient security solution; this chapter aims to enhance this capacity further. This is done by introducing low-end signal conditioning in the channel-probing step using moving-window averaging. This solution not only improves system performance but also makes it suitable for limited- power applications. The performance of the key-generation system is characterised in terms of the bit-disagreement rate between the generated key sequences of communicating parties and the key randomness by means of the NIST statistical test suite.
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.
Wireless communication systems are becoming increasingly important in our daily lives, especially with the rapid expansion of the Internet of Things (IoT). Secure and reliable communication is essential for the correct functioning of critical applications and use cases such as healthcare, finance, transportation, and industrial automation. However, the growing number of connected devices introduces significant challenges to data security and privacy due to the open nature of wireless networks. While traditional encryption methods are effective, they can be resource-intensive and impractical for resource-constrained IoT environments. Physical layer security (PLS) provides a lightweight alternative by leveraging the inherent characteristics of wireless channels to generate symmetric secret keys without requiring third-party infrastructure. The PLS key generation process involves four main stages: channel sampling, quantization, reconciliation, and privacy amplification. This paper provides a comprehensive survey of existing quantization schemes, as this step plays a crucial role in determining the key generation rate (KGR) and key disagreement rate (KDR). Additionally, the paper classifies quantization methods as either lossless or lossy, providing insights into the trade-offs between security and efficiency.
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.
Secure communication in Internet of Things (IoT) devices can be improved through physical-layer secret key generation. In secret key generation, an information reconciliation process is required to match the bit sequences between the two communicating devices. However, this process often involves complex statistical computations that increase system overhead and require the transmission of bits over an insecure public channel. Therefore, this research presents a lightweight reconciliation algorithm based on minimal bit syndrome to reduce overhead and improve key security. The proposed method processes quantized Received Signal Strength (RSS) values into 8-bit block syndrome-based reconciliation mechanism and only the 8-bit syndromes from mismatched block are transmitted over the public channel. System testing was conducted under both static and dynamic scenarios in an indoor area using Raspberry Pi nodes. The results show that the proposed scheme demonstrates an improvement in the Key Generation Rate (KGR) by 2.2% and reduces the processing time by 11.6% in dynamic scenario and 8.3% in the static scenario. Also, the proposed scheme reduce the quantization and reconciliation time by 50% compared to previous research. Furthermore, the proposed method maintained a Key Disagreement Rate (KDR) of 0% across all block size and successfully passed the NIST randomness and complexity test, with all test parameters achieving p-values above 0.01. These result indicate that the proposed scheme improve the performance of existing secret key generation methods and it is suitable for implementation in IoT devices.
The constraints imposed by the computational and energy capabilities of Internet of Things (IoT) devices are challenging for ensuring secure communication between these devices. One promising solution to address these security challenges is leveraging Physical Layer Security (PHYSEC) principles. This paper investigates in the area of PHYSEC, particularly focusing on the utilization of channel reciprocity for generating a secret key, commonly referred to as Physical Layer Secret Key Generation (PSKG), in Ultra-Wideband (UWB) technology. PSKG usually contain an Information Reconciliation (IR) stage to improve the probability for identical keys, which comes with substantial computational and communication cost. The primary objective of this paper is to eliminate this IR stage and its associated costs while still achieving a high probability for identical keys. To accomplish this goal, we propose a Pre-Processing (PP) stage involving a Discrete Fourier Transform (DFT)-based approach. This approach extracts reciprocal randomness from the Channel Impulse Response (CIR). By exploiting the CIR and our proposed PP stage, we were able to slightly improve the key generation rate while reducing the probability of successful attacks by a significant 87% in dynamic environments compared to the state-of-the-art. Notably, our approach does not require a training phase, making it more efficient for IoT devices.