The widespread presence of Internet of Things (IoT) and massive Machine Type Communications (mMTC) devices has made security management increasingly complex. Typically, security is established after authentication, which verifies the legitimacy of parties involved in data exchange. To achieve this, both symmetric and asymmetric cryptography are employed. However, asymmetric cryptography can be vulnerable to misconfigurations and cryptanalysis. It also requires more memory and processing power. In contrast, symmetric cryptography is better suited for IoT and mMTC due to its smaller memory footprint and lower susceptibility to cryptanalysis. However, it requires both parties to share a secret key and relies on complex key derivation algorithms. The distribution and management of these keys introduce additional risks, including the possibility of interception. As a result, there is extensive research into key derivation schemes that can minimize these risks and limitations. One promising approach leverages the physical communication channel, which inherently has high entropy and reciprocity, to derive private keys. In this contribution, we first propose a scheme to derive symmetric keys that minimizes information available to potential eavesdroppers, thereby enhancing system security. We then validate the scheme using both synthetic data and real-world IoT deployments-covering 2.4 GHz and 868 MHz frequencies in various indoor and outdoor mobility scenarios-achieving a 100% key agreement rate. Finally, we discuss the potential applications of this scheme in 6G umMTC (ultra mMTC) deployments.
The widespread presence of Internet of Things (IoT) and massive Machine Type Communications (mMTC) devices has made security management increasingly complex. Typically, security is established after authentication, which verifies the legitimacy of parties involved in data exchange. To achieve this, both symmetric and asymmetric cryptography are employed. However, asymmetric cryptography can be vulnerable to misconfigurations and cryptanalysis. It also requires more memory and processing power. In contrast, symmetric cryptography is better suited for IoT and mMTC due to its smaller memory footprint and lower susceptibility to cryptanalysis. However, it requires both parties to share a secret key and relies on complex key derivation algorithms. The distribution and management of these keys introduce additional risks, including the possibility of interception. As a result, there is extensive research into key derivation schemes that can minimize these risks and limitations. One promising approach leverages the physical communication channel, which inherently has high entropy and reciprocity, to derive private keys. In this contribution, we first propose a scheme to derive symmetric keys that minimizes information available to potential eavesdroppers, thereby enhancing system security. We then validate the scheme using both synthetic data and real-world IoT deployments-covering 2.4 GHz and 868 MHz frequencies in various indoor and outdoor mobility scenarios-achieving a 100% key agreement rate. Finally, we discuss the potential applications of this scheme in 6G umMTC (ultra mMTC) deployments.
As wireless networks evolve toward 5G and 6G, securing communications in dense, dynamic, and potentially adversarial environments becomes increasingly critical. Traditional cryptographic security, relying on symmetric or asymmetric key exchanges at higher layers or pre-provisioning, imposes significant operational complexity—especially in decentralized private networks and tactical deployments where infrastructure is limited and exposure to interception or cryptanalysis is high.This work introduces an environment-aware cryptographic key derivation method that utilizes 5G NR waveforms and Integrated Sensing and Communication (ISAC) principles, accompanied by updates to the security frameworks in 5G and next-generation 6G networks. It exploits radio channel reciprocity to extract highly correlated features observable only by legitimate transceivers, while inherently excluding spatially separated eavesdroppers.Our system derives shared secret keys directly from Channel State Information (CSI), achieving high-entropy keys with a 100% agreement rate and a high key generation rate between legitimate nodes. We demonstrate its robustness using software-defined radios operating in the 5G FR1 band, across diverse environments, while constraining the amount of publicly accessible information available to potential eavesdroppers.By embedding geographical awareness, this approach provides a physically secure key generation method, making it well-suited for 5G/6G systems, private networks, and ultra-massive Machine-Type Communication (umMTC) scenarios. The results highlight the potential of physical layer security for future wireless encryption frameworks with design considerations to maximize security and limit eavesdropping feasibility.
Widespread wireless communication demands robust security management to prevent adversaries from exploiting ubiquitous radio signals. Traditionally, such security is enforced through mitigation strategies implemented at higher layers using symmetric and/or asymmetric cryptography. However, this introduces increasing complexity, particularly due to the key derivation and exchange mechanisms required. This challenge is further exacerbated in private networks, which often necessitate configuration and deployment at the tactical or ad-hoc level in hostile environments, where physical interception or cryptanalysis by adversaries is a significant risk.To address this, we propose a solution that performs key derivation by exploiting 5G waveforms and integrated sensing and communication (ISAC). This approach enables the extraction of highly correlated data between legitimate parties by leveraging the reciprocity of radio channels, while preventing eavesdroppers from replicating the key derivation process.In this demonstration, we show that incorporating physical-layer information introduces geographical awareness between communicating parties, without compromising the entropy and keys generation rate. Furthermore, we demonstrate that our method effectively prevents eavesdroppers from obtaining the information needed to compromise the derived keys. Finally, we implement the system using software-defined radio within the 5G FR1 frequency bands, achieving a 100% key agreement rate with high entropy across a range of environments.
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.
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.
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 growth of IoT in 6G networks necessitates efficient, lightweight security solutions for generating secure symmetric keys in low-power devices, as traditional public-key cryptography is inadequate due to complexity and overhead. In this paper, we propose a novel approach for physical layer key generation using channel reciprocity-based methods. Our contribution lies in the development of a unique feature-extraction method that reduces signal dimensions, enhances computational efficiency, and improves shared key accuracy. Additionally, we introduce an innovative Equalized Fuzzy C-Means (EFCM) vector quantization scheme that ensures clusters of equal size and maximizes entropy and randomness. Existing vector quantization schemes have communication overhead to address high Bit Error Rate (BER) through the exchanging of information over the channel. This can cause information leakage to a potential eavesdropper, which is addressed in our scheme by eliminating this communication overhead. Simulation results demonstrate the superiority of our approach, achieving higher key entropy, lower BER, and improved security compared to traditional scalar quantization and Principal Component Analysis-KMeans (PCA-KM) methods. The proposed scheme offers a promising solution for secure key generation in 6G networks and addresses the challenges posed by IoT devices' limited capabilities.