The confidentiality of communications in the Internet of Things (IoT) is critical, with cryptography currently being the most widely employed method of ensuring it. Establishing cryptographically secure communication links between two transceivers requires the pre-agreement on some key, unknown to an external attacker. In recent years there has been growing attention in techniques that generate a shared random key through observation of the channel and its effects on the exchanged messages. In this work we present SKYGlow, a novel scheme for secret-key generation, designed for IoT devices, such as IEEE 802.15.4 and Bluetooth Low Energy (BLE) transceivers. SKYGlow employs the Discreet Cosine Transform (DCT) of channel observations and Slepian-Wolf coding for information reconciliation. Real-life experiments have resulted in the creation of 128-bit secret keys with only 65 packet exchanges and with an entropy of 0.9978 bits, making our scheme much more energy-efficient compared with others in the existing literature.
The confidentiality of communications in the Internet of Things (IoT) is critical, with cryptography currently being the most widely employed method of ensuring it. Establishing cryptographically secure communication links between two transceivers requires the pre-agreement on some key, unknown to an external attacker. In recent years there has been growing attention in techniques that generate a shared random key through observation of the channel and its effects on the exchanged messages. In this work we present SKYGlow, a novel scheme for secret-key generation, designed for IoT devices, such as IEEE 802.15.4 and Bluetooth Low Energy (BLE) transceivers. SKYGlow employs the Discreet Cosine Transform (DCT) of channel observations and Slepian-Wolf coding for information reconciliation. Real-life experiments have resulted in the creation of 128-bit secret keys with only 65 packet exchanges and with an entropy of 0.9978 bits, making our scheme much more energy-efficient compared with others in the existing literature.
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.
The billions of IoT devices in our home, healthcare, and industry rely on cryptographic keys for security. Constrained by computing and power resources, their keys are often pre-shared and rarely updated, creating a major vulnerability. The physical-layer wireless key generation can efficiently update keys but requires fluctuating wireless channel conditions, which, however, are scarce in static deployments. Here we show a novel wireless key generation that produces secure keys for static IoT without hardware assistance or secret exchange. We propose a randomness that is composed of received signal strengths and locally selected transmit power levels; the latter are independently selected at both communication pairs and are never exchanged, resulting in distinctive received signal strengths to ensure secrecy. We propose quantization and entropy balancing to boost generation efficiency and consistency. Experiments on an IoT testbed across multiple static scenarios achieve a fast key generation rate of 9.47 bits per second and a low key disagreement rate of 0.05, while defending against eavesdropping attacks. Xintao Huan and colleagues report a physical-layer wireless key generation scheme for static IoT scenarios. This scheme does not require hardware assistance or secret exchange and achieves fast key generation in static IoT while defending against eavesdropping attacks.
The confidentiality of communications in the Internet of Things (IoT) is critical, with cryptography being currently the most widely employed method to achieve it. Establishing cryptographically secure communication links between two transceivers requires the pre-agreement on some key, unknown to an external attacker. In recent years there has been growing interest for techniques that generate a shared random key through observation of the channel and its effects on the exchanged messages. The maximum length of that key is characterised by the Mutual Information (MI) between the observations of the two radios. In this work we examine the practical limits of the MI of off-the-shelf transceivers communicating through the IEEE 802.15.4 specification in an indoor office environment, and calculate the secret-key capacity, that is, the maximum length of an extracted secret-key in the presence of an eavesdropper. Furthermore, we study how using groups of observations can affect the MI and both analytically and experimentally prove that grouping observations leads to better results and an increased key-capacity.
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.
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.
The constraints imposed by the computational and energy capabilities of Internet of Things (IoT) devices are challenging for ensuring secure communication between these devices. One promising solution to address these security challenges is leveraging Physical Layer Security (PHYSEC) principles. This paper investigates in the area of PHYSEC, particularly focusing on the utilization of channel reciprocity for generating a secret key, commonly referred to as Physical Layer Secret Key Generation (PSKG), in Ultra-Wideband (UWB) technology. PSKG usually contain an Information Reconciliation (IR) stage to improve the probability for identical keys, which comes with substantial computational and communication cost. The primary objective of this paper is to eliminate this IR stage and its associated costs while still achieving a high probability for identical keys. To accomplish this goal, we propose a Pre-Processing (PP) stage involving a Discrete Fourier Transform (DFT)-based approach. This approach extracts reciprocal randomness from the Channel Impulse Response (CIR). By exploiting the CIR and our proposed PP stage, we were able to slightly improve the key generation rate while reducing the probability of successful attacks by a significant 87% in dynamic environments compared to the state-of-the-art. Notably, our approach does not require a training phase, making it more efficient for IoT devices.