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International Journal For Multidisciplinary Research

Proficient Secret Key Generation using Derivates of the Transform Function for the Internet of Things

Reeta Singh -, Susheel Kumar Tiwari

Published Feb 29, 2024• 1 citation
Publisher

Abstract

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.

DOI
10.36948/ijfmr.2024.v06i01.14260

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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.
Security and privacy of data are important factors in the Internet of Things (IoT), which enables safe communication with emerging objects.The non-standardization of security protocols on the internet of things makes them vulnerable to intruders and compromises the process of communication.Conventional cryptography approaches face certain limitations in the generation of keys for authentication and authorization of communication systems.The limitations of the cryptography approach are explored in terms of computational overhead and hardware requirements.Singal processing is an alternative cryptography approach for key generation in physical-layer wireless communication systems.The wireless channel's reciprocity principle is used to generate keys.The important factor in signal-based key generation is the process of quantization.In this manuscript, we propose modified discrete wavelet transform (DWT) methods for key generation in IoT.The modification of discrete wavelet transform methods encapsulates the process of common spatial patterning (CSP).The CSP is another signal processing method for the formation of signal patterns.The proposed method reduces DWT discrepancies and yields a key generation technique that is efficient.Key generation processing used multi-bit quantization to quantify the product of the modified discrete wavelet transform.The proposed algorithm for key generation simulates different numbers of device nodes in indoor and outdoor scenarios.Use MATLAB tools to simulate the process and measure standard parameters such as AKL and KDR.According to the results, the suggested technique outperforms existing key generation algorithms by 3%.
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.
This article presents a novel low-complexity deep learning model for physical-layer secret key generation (PSKG), specifically designed to enhance wireless security in the Internet of Things (IoT). PSKG typically generates cryptographic keys by exploiting the reciprocal nature of wireless channels; however, this reciprocity is frequently compromised in time-division duplex (TDD) systems due to hardware imperfections and noise, which significantly complicate the key generation process. To effectively address these challenges, the proposed deep neural network (DNN) is developed to efficiently learn and enhance reciprocity features, even in the presence of imperfect channel state information (CSI). Furthermore, the study introduces a DNN-based PSKG method that strategically leverages the phase of channel frequency responses for key extraction. The results conclusively demonstrate that the proposed model substantially reduces the key disagreement ratio (KDR) and enhances randomness, thus providing a robust and practical solution for securing wireless communications in IoT environments.
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.
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.
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.