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.
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.
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.
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.
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.
With the increasing demands for sharing confidential information among massive Internet of Things (IoT) devices in 5G and beyond wireless networks, many applications require the common secret key generation for a group of IoT devices. However, most of the existing works on physical layer secret key generation (PLKG) only focus on the pairwise key generation between two users, which is a low efficient and high cost to be extended to the scenarios of group key generation. In this work, we propose a new efficient multiple-input–multiple-output (MIMO) physical layer group secret key generation scheme to reduce the consumption of channel probing and improve the efficiency for group key generation. Different from current schemes, in the proposed scheme, the transmitter randomly generates the group secret key and directly broadcasts the downlink data symbols to the group users. At the receiver end, each group user can efficiently “observe” the common group key through the downlink broadcasting data symbols, while keeping perfect secrecy of the shared group key against eavesdroppers. The performance of reliability, security, and the group key generation rate is fully investigated, which shows the advantages of high efficiency, low consumption, and strong robustness of the proposed scheme. Extensive simulations are conducted to validate the effectiveness of the proposed scheme.
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.
With the burgeoning internet of things (IoT), securing communication becomes paramount. Traditional cryptography does not meet computational needs and brute-force attacks. This review explores the state-of-the-art physical layer secret key generation (PLKG) that takes advantage of the inherent reciprocity and randomness of wireless channels. We investigate cutting-edge techniques such as feature extraction networks, domain adversarial training, and deep learning-based approaches, evaluating their effects on the security and efficiency of key generation. In addition to these methods, the review addresses real-world challenges such as multi-user scenarios, reconciliation overhead, and inconsistent channel measurement. We believe that improved key generation rates and security can be achieved through the use of millimeter wave technology and full-duplex communication. To strengthen the robustness of key generation, the paper concludes by suggesting future directions, such as incorporating more random sources, such as physiological signals and sensor data. This comprehensive overview offers deep insights into the state-of-the-art and paves the way for reliable communication in ever more complicated IoT settings.