Physical layer key generation exploiting inherent channel randomness is an open research area in securing the networks with resource constraint nodes; therefore reduction of numerical computation is desirable to save battery power. However, the correlated components due to colored noise also affect the system performance. In this work, we consider the correlated colored noise components along with the additive white Gaussian noise (AWGN) in the wireless channel and analyze the effect of these correlated components on the system performance. We further propose a hybrid averaging and dimensionality reduction (AvDR), based received signal strength (RSS) preprocessing which is the combination of moving window averaging (Av) and principal component analysis (PCA) as dimensionality reduction technique (DR) to improve the system performance. Further, the system performance was evaluated by numerical simulations, and it is observed that the same improvement in system performance is achieved by generating keys from a fewer number of points selected after PCA as compared to processing all the points. Picking a few of the points in the data sequence instead of all reduces the total number of numerical calculations and saves system power, which is the primary requirement of resource constraint networks like the IoT.
Physical layer key generation exploiting inherent channel randomness is an open research area in securing the networks with resource constraint nodes; therefore reduction of numerical computation is desirable to save battery power. However, the correlated components due to colored noise also affect the system performance. In this work, we consider the correlated colored noise components along with the additive white Gaussian noise (AWGN) in the wireless channel and analyze the effect of these correlated components on the system performance. We further propose a hybrid averaging and dimensionality reduction (AvDR), based received signal strength (RSS) preprocessing which is the combination of moving window averaging (Av) and principal component analysis (PCA) as dimensionality reduction technique (DR) to improve the system performance. Further, the system performance was evaluated by numerical simulations, and it is observed that the same improvement in system performance is achieved by generating keys from a fewer number of points selected after PCA as compared to processing all the points. Picking a few of the points in the data sequence instead of all reduces the total number of numerical calculations and saves system power, which is the primary requirement of resource constraint networks like the IoT.
Summary Achieving security in the Internet of things (IoT) networks by generating symmetric keys from the wireless channel parameters like received signal strength (RSS) is a promising approach. Despite the easy acquisition of the RSS signal, RSS‐based security is less explored for IoT. In this work, we analyze the performance of RSS‐based wireless physical layer key generation with correlated colored noise components and proposed a low complexity filtering approach to improve the performance for the IoT network. We started with providing a survey of various recent researches related to RSS‐based key generation and also discussed correlated colored noise components with a few of the recent works considering them. Further, we analyze various colored noise components in the time domain by the Allan variance and Ljung‐Box test. Furthermore, we develop a key generation model and proposed a moving window averaging‐based filtering followed by Lloyd max quantization to improve the BDR performance, degraded due to the presence of correlated colored noise components. The simulation results show that the proposed preprocessing technique has a considerable improvement in the BDR performance, and the keys generated have sufficient randomness, which is verified by NIST test.
Achieving security in the wireless networks by extracting secure bits using physical layer key generation is one of the emerging ideas. However, for the networks with the resource constraint nodes, the security algorithm should be less computationally complex. This can be achieved by reducing the number of numerical computations or by reducing the dimensionality of input data. In this work, we propose to apply dimensionality reduction by the principal component analysis on the input vector of received signal strength for key generation. We select the principal components extracted from the input vector based on information content & cross correlation, and instead of considering all the column of input vector we consider the column vectors corresponding to a few dominant principal components only, for secure key generation. The bit disagreement performance of the system is improved along with the reduction in several numerical calculations corresponding to the rejected input vectors at the cost of few extra computations. This reduces the computational power significantly as verified by simulation and experimental results with sufficient 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.
Abstract: Physical layer key generation, leveraging the reciprocity and randomness of wireless fading channels, has garnered significant research interest in recent years. While theoretical studies have demonstrated its potential to generate informationtheoretically secure keys, challenges remain in translating theory into practice. This paper provides an overview of the physical layer key generation process and discusses its practical challenges. Various passive and active attacks are analyzed and evaluated through numerical studies. A new key generation scheme using random probing signals and combining usergenerated randomness with channel randomness is introduced to counteract active attacks. Numerical results demonstrate that the proposed scheme achieves higher security strength than existing schemes using constant probing signals under active attacks. Secret-key generation relying on wireless channel reciprocity is an intriguing solution as it can be efficiently implemented at the physical layer of emerging wireless communication networks, while providing information-theoretic security guarantees. In this paper, we investigate and compare the secret-key capacity based on the sampling of the entire complex channel state information (CSI) or only its envelope, the received signal strength (RSS).
Summary There has been growing attention in physical layer security (PLS) techniques for wireless networks. PLS techniques are based on physical layer characteristics and are less computationally complex than traditional cryptographic approaches, which work on higher layers of networks. Physical layer key generation (PLKG) is one of the prominent key based PLS techniques that exploits reciprocity and randomness of wireless fading channel to establish secure communication between two transceivers. PLKG system consists of five stages as RSS measurement, its preprocessing, quantization and encoding, key reconciliation, and privacy amplification. Each stage has a substantial role in deciding the performance of PLKG system, but RSS preprocessing offers wide scope in performance improvement for the system. In this work, we propose PLKG system using a new class of preprocessing technique first time, which is nonlinear. We formulated the problem uniquely and considered two nonlinear techniques: nonlinear preprocessing using expander (NLPE) and nonlinear preprocessing using compressor (NLPC). We analyzed the performance of system using NLPE and NLPC and found NLPE is most suitable. Performance of the system is evaluated in terms of bit disagreement rate (BDR) and randomness of keys. Results show that NLPE performs extremely well, as compared with linear approach and without preprocessing approach, especially at low signal to noise ratio (SNR) range. Emerging wireless networks such as internet of things (IoT), centralized radio access network (CRAN), wireless sensor network (WSN), and so forth are being operated at lower transmission power with lightweight hardware. Hence, for such type of power constraint networks, proposed PLKG system with NLPE can be proven a perfect security solution.
For the future Internet of Things (IoT) with ubiquitous wireless connectivity, the physical layer secret key (PLSK) scheme has been recognized as an effective and lightweight way to achieve secure communications by exploiting the reciprocity of the wireless channel. Compared with channel state information (CSI)-based PLSK generation schemes, received signal strength (RSS)-based schemes are more advantageous in terms of the communication costs and mobility for the IoT applications. However, due to the vulnerability of predictable channel attacks, RSS-based schemes cannot always guarantee perfect security. In this paper, we combine angle of arrival (AoA) and received signal strength (RSS) to generate physical layer secret key (PLSK). In particular, we propose a novel measurement named effective key generation rate (EKGR) to comprehensively and directly evaluate a PLSK scheme. Meanwhile, we further simplify the conventional PLSK generation steps to meet the low-complexity demand of IoT devices. At last, numerical results demonstrate that comparing with RSS-based and AoA-based schemes, our proposed EKGR of the RSS-AoA-based scheme achieves 78% or even 120% performance improvement.