The Internet of Things (IoT) features pervasive sensing and control capabilities by deploying a massive machine-type communication (MTC) devices. However, low hardware complexity, low processing capacity and constrained energy of MTC devices raise lots of challenges to communication and security. Therefore, physical layer security (PLS) appears to be a promising method for securing wireless communication in which orthogonal frequency division multiplexing (OFDM) is widely used. However, most existing OFDM-based PLS schemes possess little dynamicity for their secret keys derived from pre-extracted information. The combination of OFDM and compressed sensing (CS) is a remarkable approach to address the weakness. So, with compressed sensing, we propose a physical layer security scheme for OFDM-based IoT systems, named PLSSCS. It can address the demerit of key extraction by channel measuring instead of pre-extracted information. To evaluate the performance of PLSSCS, the simulation is carried out in MATLAB, and PLSSCS provides better performance with dynamic property.
The Internet of Things (IoT) features pervasive sensing and control capabilities by deploying a massive machine-type communication (MTC) devices. However, low hardware complexity, low processing capacity and constrained energy of MTC devices raise lots of challenges to communication and security. Therefore, physical layer security (PLS) appears to be a promising method for securing wireless communication in which orthogonal frequency division multiplexing (OFDM) is widely used. However, most existing OFDM-based PLS schemes possess little dynamicity for their secret keys derived from pre-extracted information. The combination of OFDM and compressed sensing (CS) is a remarkable approach to address the weakness. So, with compressed sensing, we propose a physical layer security scheme for OFDM-based IoT systems, named PLSSCS. It can address the demerit of key extraction by channel measuring instead of pre-extracted information. To evaluate the performance of PLSSCS, the simulation is carried out in MATLAB, and PLSSCS provides better performance with dynamic property.
As aspects of our daily lives become more interconnected with the emergence of the Internet of Things (IoT), it is imperative that our devices are reliable and secure from threats. Vulnerabilities of Wi-Fi Protected Access (WPA/WPA2) have been exposed in the past, motivating the use of multiple security techniques, even with the release of WPA3. Physical layer security leverages existing components of communication systems to enable methods of protecting devices that are well-suited for IoT applications. In this work, we provide a low-complexity technique for generating secret keys at the Physical layer to enable improved IoT security. We leverage the existing carrier frequency offset (CFO) and channel estimation components of Orthogonal Frequency Division Multiplexing (OFDM) receivers for an efficient approach. The key generation algorithm we propose focuses on the unique CFO and channel experienced between a pair of desired nodes, and to the best of our understanding, the combination of the features has not been examined previously for the purpose of secret key generation. Our techniques are appropriate for IoT devices, as they do not require extensive processing capabilities and are based on second order statistics. We obtain experimental results using USRP N210 software defined radios and analyze the performance of our methods in post-processing. Our techniques improve the capability of desired nodes to establish matching secret keys, while hindering the threat of an eavesdropper, and are useful for protecting future IoT devices.
The increasing demands for Internet of things (IoT) applications and the tremendous increase in the volume of IoT generated data bring novel challenges for the fifth generation (5G) network. Verticals such as e-Health, vehicle to everything (V2X) and unmanned aerial vehicles (UAVs) require solutions that can guarantee low latency, energy efficiency,massive connectivity, and high reliability. In particular, finding strong security mechanisms that satisfy the above is of central importance for bringing the IoT to life. \nIn this regards, employing physical layer security (PLS) methods could be greatly beneficial for IoT networks. While current security solutions rely on computational complexity, PLS is based on information theoretic proofs. By removing the need for computational power, PLS is ideally suited for resource constrained devices. In detail, PLS can ensure security using the inherit randomness already present in the physical channel. Promising schemes from the physical layer include physical unclonable functions (PUFs), which are seen as the hardware fingerprint of a device, and secret key generation (SKG) from wireless fading coefficients, which provide the wireless fingerprint of the communication channel between devices. \nThe present thesis develops several PLS-based techniques that pave the way for a new breed of latency-aware, lightweight, security protocols. In particular, the work proposes: i) a fast multi-factor authentication solution with verified security properties based on PUFs, proximity detection and SKG; ii) an authenticated encryption SKG approach that interweaves data transmission and key generation; and, iii) a set of countermeasures to man-in-the-middle and jamming attacks. Overall, PLS solutions show promising performance, especially in the context of IoT applications, therefore, the advances in this thesis should be considered for beyond-5G networks.
Information security is a vital concern in Internet of Things (IoT). Traditional security method based on public or private key encryption scheme is limited by the trade‐off between low cost and high level of security. Among different security solutions, utilising compressed sensing (CS) in combination with the physical‐layer security to achieve the security is a remarkable method. However, in the current literatures, little attention has been given to the area of static environment, which will lead the risk of information leakage in the CS security model. In this study, the authors propose a new CS security model, in which circulant matrix is exploited to improve the generation efficiency of the measurement matrix, and binary resilient functions are utilised to enhance the security. Furthermore, considering the practical application, they present a feasible framework, named CS security scheme based on frequency‐selective, where the frequency‐selective feature of the wireless channel is applied to support the static environment. To verify the effectiveness of the proposed scheme, they conducted experiments and numerical simulations to evaluate the performance, and the results are satisfactory.
The pervasiveness of commercial Internet of Things (IoT) around the globe is expected to reach significant levels with the upcoming sixth generation of mobile networks (6G). Throughout the past years, wireless standardization units worldwide have been prominently active in the deployment and performance optimization of such IoT networks and fusing them with current and futuristic cellular networks. Nonetheless, the openness of wireless transmissions and the forecasted overwhelm in connected devices will provoke unprecedented security leakages and vulnerabilities. In addition to the key targets of the 6G and IoT, it has been of paramount importance to cater to decent and lightweight security mechanisms in ultra-massively connected heterogeneous networks. Recently, significant efforts have been made to pave the way for the integration of physical layer security (PLS) in contemporary and futuristic networks. The primary motivation behind its deployment resides in its low complexity and ability to provide information-theoretic secure transmissions, which alleviates the complexity burden caused by implementing complex cryptographic schemes. This survey overviews the recent advancement in PLS techniques with a particular interest in its application to the Internet of Things (IoT). We review essentially recent PLS techniques aiming at ensuring message confidentiality along with node/message authentication and malicious nodes’ detection, where their corresponding application scenarios and underlying pros and cons are discussed. On top of that, we explore recent findings in the incorporation of cutting-edge technologies at the physical layer, such as non-orthogonal multiple-access, reconfigurable intelligent surfaces, joint communication and sensing, and optical wireless/Terahertz communications in boosting confidentiality and authentication at the physical layer. Lastly, promising extensions and future directions are discussed based on the quantified pros and cons of each PLS category, opening up ways for timely research directions within the topic and current/future challenges faced by PLS.
In this chapter, the physical layer security (PLS) is interrogated for Internet of things (IoT) networks. As a result of their nature, IoT networks consist of a massive number of devices. These devices are produced in compact sizes and powered with small batteries rather than a grid network. Thus, they are able to have high portability but have low computational capability. Because of these features of IoT devices, the conventional encryption/decryption-based cryptographic security methods used in the classical wireless devices fail to satisfy the security requirements of IoT networks. The main reason for this is that the requirements of these security methods are too demanding for the processing power of IoT devices and stringent to manage when these devices are deployed in a massive number. Therefore, in this chapter, the possible advantages of the PLS in IoT networks are explained. In this manner, the IoT architecture is explained and common attack types in IoT networks are reviewed. Then, the unique features of IoT networks from the PLS perspective are presented along with the possible challenges on the way. A literature review is conducted to propound the current PLS techniques used in IoT networks and the future directions are signified. As a result of this chapter, it is expected that PLS techniques in IoT networks are clarified to the reader and their future applications are encouraged.
As a promising technology, physical layer security (PLS) enhances security by leveraging the physical characteristics of communication channels. However, it commonly takes the legitimate user more effort to secure its data, compared to that required by the eavesdropper to intercept the communication. To address this imbalance, we propose a physical layer deception (PLD) framework, which applies random deceptive ciphering combined with orthogonal frequency-division multiplexing (OFDM) to deceive eavesdroppers with falsified information, preventing them from wiretapping. While ensuring the same level of confidentiality as traditional PLS methods, the PLD approach additionally introduces a deception mechanism, which remains effective even when the eavesdropper has the same knowledge about the transmitter as the legitimate receiver. Through detailed theoretical analysis and numerical simulations, we prove the superiority of our method over the conventional PLS approach.