Security and privacy of data are important factors in the Internet of Things (IoT), which enables safe communication with emerging objects.The non-standardization of security protocols on the internet of things makes them vulnerable to intruders and compromises the process of communication.Conventional cryptography approaches face certain limitations in the generation of keys for authentication and authorization of communication systems.The limitations of the cryptography approach are explored in terms of computational overhead and hardware requirements.Singal processing is an alternative cryptography approach for key generation in physical-layer wireless communication systems.The wireless channel's reciprocity principle is used to generate keys.The important factor in signal-based key generation is the process of quantization.In this manuscript, we propose modified discrete wavelet transform (DWT) methods for key generation in IoT.The modification of discrete wavelet transform methods encapsulates the process of common spatial patterning (CSP).The CSP is another signal processing method for the formation of signal patterns.The proposed method reduces DWT discrepancies and yields a key generation technique that is efficient.Key generation processing used multi-bit quantization to quantify the product of the modified discrete wavelet transform.The proposed algorithm for key generation simulates different numbers of device nodes in indoor and outdoor scenarios.Use MATLAB tools to simulate the process and measure standard parameters such as AKL and KDR.According to the results, the suggested technique outperforms existing key generation algorithms by 3%.
Security and privacy of data are important factors in the Internet of Things (IoT), which enables safe communication with emerging objects.The non-standardization of security protocols on the internet of things makes them vulnerable to intruders and compromises the process of communication.Conventional cryptography approaches face certain limitations in the generation of keys for authentication and authorization of communication systems.The limitations of the cryptography approach are explored in terms of computational overhead and hardware requirements.Singal processing is an alternative cryptography approach for key generation in physical-layer wireless communication systems.The wireless channel's reciprocity principle is used to generate keys.The important factor in signal-based key generation is the process of quantization.In this manuscript, we propose modified discrete wavelet transform (DWT) methods for key generation in IoT.The modification of discrete wavelet transform methods encapsulates the process of common spatial patterning (CSP).The CSP is another signal processing method for the formation of signal patterns.The proposed method reduces DWT discrepancies and yields a key generation technique that is efficient.Key generation processing used multi-bit quantization to quantify the product of the modified discrete wavelet transform.The proposed algorithm for key generation simulates different numbers of device nodes in indoor and outdoor scenarios.Use MATLAB tools to simulate the process and measure standard parameters such as AKL and KDR.According to the results, the suggested technique outperforms existing key generation algorithms by 3%.
Secured data transfer over the internet of things enables communication devices is a very challenging task. Due to the limitation of computational resources, classical cryptography algorithms are vulnerable to cyber threats and loss of integrity and information. Recently, physical layer secret key generation has played a vital role in secured communication on the internet of things. The physical layer key generation approach provides an alternate means of information security using the signal reciprocity of wireless communication. This paper proposed proficient dynamic secret key generation using derivates of transform functions. The derivates of transform functions such as DCT, DWT, DWPT, and many more light transform functions the physical layer key generation employs RSSI channel parameters in conjunction with the discrete wavelet transform to generate low computational secret keys for the authentication of IoT-enabled communication devices. The proposed algorithm is very efficient and reduces the bit disagreement of the key. We use MATLAB software defined radios to conduct the experiment, and we then evaluate the effectiveness of our post-processing techniques. Our approach is helpful for safeguarding future IoT devices since it enhances the ability of targeted nodes to establish matching secret keys while reducing the risk of an eavesdropper. The experimental results of key generation methods suggest that the proposed algorithm is very efficient instead of DCT and DWT.
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.
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.
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.
One solution to ensure secrecy in the Internet of Things (IoT) is cryptography. However, classical cryptographic systems require high computational complexity that is not appropriate for IoT devices with restricted computing resources, energy, and memory. Physical layer security that utilizes channel characteristics is an often used solution because it is simpler and more efficient than classical cryptographic systems. In this paper, we propose a signal strength exchange (SSE) system as an efficient key generation system and a synchronized quantization (SQ) method as a part of the SSE system that synchronizes data blocks in the quantization phase. The SQ method eliminates the signal pre-processing phase by performing a multi-bit conversion directly from the channel characteristics of the measurement results. Synchronization is carried out between the two authorized nodes to ensure sameness of the produced keys so it can eliminate the error-correcting phase. The test results at the IoT devices equipped with IEEE 802.11 radio show that SSE system is more efficient in terms of computing time and communication overhead than existing systems.
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.