Securing Medical Information Transmission Between IoT Devices: An Innovative Hybrid Encryption Scheme Based on Quantum Walk, DNA Encoding, and Chaos

The healthcare industry has undergone a transformation due to the widespread use of advanced communication technologies and wireless sensor networks such as the Internet of Medical Things (IoMT), Health Information Exchange Technology (HIET), Internet of Healthcare Things (IoHT) and Health IoT (HIoT). These technologies have led to an increase in the transmission of medical data, particularly medical imaging data, over various wireless communication channels. However, transmitting high-quality color medical images over insecure internet channels like the Internet and communication networks like 5G presents significant security risks that could threaten patients’ data privacy. Furthermore, this process can also burden the limited bandwidth of the communication channel, leading to delayed data transmission. To address security concerns in healthcare data, researchers have focused a lot of attention on medical image encryption as a means of protecting patient data. This paper presents a color image encryption scheme that integrates multiple encryption techniques, including alternate quantum random walks, controlled Rubik’s Cube transformations, and the integration of the Elliptic Curve Cryptosystem with Hill Cipher (ECCHC). The proposed scheme divides various plaintext images by creating a regular cube by layering planes of a fixed size. Each plane is rotated in an anticlockwise direction, followed by row, column and face swapping, and then DNA encoding is performed. The image cube encoded with DNA is combined with the chaotic cube through DNA addition, and a couple of random DNA sequences are chosen for DNA mutation. After undergoing DNA mutation, the encoded cube is then decoded using DNA. The proposed method has the theoretical capability of encrypting 2D images of unlimited size and number by utilizing an infinitely large cube. The proposed image encryption scheme has been rigorously tested through various experimental simulations and cyberattack analysis, which shows the efficiency and reliability of the proposed encryption scheme

Dynamic Substitution and Confusion-Diffusion-Based Noise-Resistive Image Encryption Using Multiple Chaotic Maps

The advancement in wireless communication has encouraged the process of data transferring through the Internet. The process of data sharing via the Internet is prone to several attacks. The sensitive information can be protected from hackers with the help of a process called Encryption. Owing to the increase in cyber-attacks, encryption has become a vital component of modern-day communication. In this article, an image encryption algorithm is suggested using dynamic substitution and chaotic systems. The suggested scheme is based upon the chaotic logistic map, chaotic sine maps and the dynamical substitution boxes (S-boxes). In the proposed scheme, the S-box selection is according to the generated sequence by deploying the chaotic sine map. To evaluate the robustness and security of the proposed encryption scheme, different security analysis like correlation analysis, information entropy, energy, histogram investigation, and mean square error are performed. The keyspace and entropy values of the enciphered images generated through the proposed encryption scheme are over 2 278 and 7.99 respectively. Moreover, the correlation values are closer to zero after comparison with the other existing schemes. The unified average change intensity (UACI) and the number of pixel change rate (NPCR) for the suggested scheme are greater than 33, 99.50% respectively. The simulation outcomes and the balancing with state-of-the-art algorithms justify the security and efficiency of the suggested schem

Data security in the Industrial Internet of Things (IIoT) through a triple-image encryption framework leveraging 3-D NEAT, 1DCJ, and 4DHCFO techniques

In the Industrial Internet of Things (IIoT) era, protecting vast data volumes, including sensitive information, poses a significant security challenge. To address this issue, this research proposes a novel triple-image encryption method tailored for IIoT applications. Unlike conventional algorithms processing a single grayscale image to produce a corresponding single ciphertext, the proposed approach generates a single color encrypted image corresponding to three grayscale input images. This complexity adds an extra layer of challenge for unauthorized individuals attempting to recover plaintext data. Leveraging the 3-D non-equilateral Arnold transform (NEAT), extended one-dimensional chaotic jumping (1DCJ), and a four-dimensional hyperchaotic Chen map of fractional order (4DHCFO), the proposed method begins by processing three grayscale images—R gray, G gray, and B gray—with a 3-D NEAT to scramble their pixel positions. Employing three distinct scrambling operations, multilayer permutation, multiround permutation, and diagonal permutation, enhances scrambling complexity. Subsequently, binary bit planes are extracted and subjected to bit-level scrambling via 1DCJ. Further, a 4DHCFO generates a 16 × 16 substitution box for diffusing scrambled bit planes using XOR operations. Experimental analyses encompassing entropy, correlation, energy, histogram, key sensitivity, key space, NPCR, and UACI reveal the efficacy of the proposed scheme. The scheme demonstrates significant statistical values (entropy: 7.9999, correlation: 0.0001, NPCR: 33.96, UACI: 96.79) and operates efficiently with a computational time of 0.002 for encrypting triple grayscale images simultaneously which shows its suitability for real-time applications

Noise-Resistant Image Encryption Scheme for Medical Images in the Chaos and Wavelet Domain

In this paper, a noise-resistant image encryption scheme is proposed. We have used a cubic-logistic map, Discrete Wavelet Transform (DWT), and bit-plane extraction method to encrypt the medical images at the bit-level rather than pixel-level. The proposed work is divided into three sections; In the first and the last section, the image is encrypted in the spatial domain. While the middle section of the proposed algorithm is devoted to the frequency domain encryption in which DWT is incorporated. As the frequency domain encryption section is a sandwich between the two spatial domain encryption sections, we called it a ”sandwich encryption.” The proposed algorithm is lossless because it can decrypt the exact pixel values of an image. Along with this, we have also gauge the proposed scheme's performance using statistical analysis such as entropy, correlation, and contrast. The entropy values of the cipher images generated from the proposed encryption scheme are more remarkable than 7.99, while correlation values are very close to zero. Furthermore, the number of pixel change rate (NPCR) and unified average change intensity (UACI) for the proposed encryption scheme is higher than 99.4% and 33, respectively. We have also tested the proposed algorithm by performing attacks such as cropping and noise attacks on enciphered images, and we found that the proposed algorithm can decrypt the plaintext image with little loss of information, but the content of the original image is visible

A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in real-time applications

Efficient and secure image encryption using key substitution process with discrete wavelet transform

Over the past few years, there has been a rise in the utilization of chaotic encryption algorithms for securing images. The majority of chaos-based encryption algorithms adhere to the conventional model of confusion and diffusion, which typically involves either implementing multiple encryption rounds or employing a single round of intricate encryption to guarantee robust security. However, such kind of approaches reduces the computational efficiency of the encryption process but compromises security. There is a trade-off between security and computational efficiency. Prioritizing security may require high computational processes. To overcome this issue, a key substitution encryption process with discrete wavelet transform (KSP-DWT) is developed in the proposed image encryption technique (IET). Based on KSP-DWT and IET, the abbreviation of the proposed work is used in this paper as KSP-DWT-IET. The proposed KSP-DWT algorithm employs a key scheming technique to update the initial keys and uses a novel substitution method to encrypt digital images of different sizes. Additionally, the integration of DWT can result in the compression of frequency sub-bands of the source image, leading to lower computational overheads without compromising the security of the encryption. The KSP-DWT-IET performs a single encryption round and is highly secure and efficient. The simulation results and security analysis conducted on KSP-DWT-IET confirm its effectiveness in ensuring high-security image encryption while minimizing computational overhead. The proposed encryption technique undergoes various security analyses, including entropy, contrast, correlation, energy, NPCR (Number of Pixel Changes Rate), UACI (Unified Average Change Intensity) and computational complexity. The statistical values obtained for such parameters are 7.9991, 10.9889, 0.0001, 0.0152, 33.6767, and 33.6899, respectively, which indicate that the encryption technique performs very well in terms of security and computational efficiency. The proposed encryption scheme is also analyzed for its computational time in addition to its security. The analysis shows that the scheme can efficiently encrypt images of varying sizes with a high level of security in a short amount of time (i.e., 2 ms). Therefore, it is feasible to use this encryption scheme in real-time applications without causing any significant delays. Moreover, the key space of the proposed encryption scheme is large enough (i.e. Keyspace ) to resist the brute force attack

Future Forecasting of COVID-19: A Supervised Learning Approach

A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy

Detecting the Security Level of Various Cryptosystems Using Machine Learning Models

With recent advancements in multimedia technologies, the security of digital data has become a critical issue. To overcome the vulnerabilities of current security protocols, researchers tend to focus their efforts on modifying existing protocols. Over the last few decades, though, several proposed encryption algorithms have been proven insecure, leading to major threats against important data. Using the most appropriate encryption algorithm is a very important means of protection against such attacks, but which algorithm is most appropriate in any particular situation will also be dependent on what sort of data is being secured. However, testing potential cryptosystems one by one to find the best option can take up an important processing time. For a fast and accurate selection of appropriate encryption algorithms, we propose a security level detection approach for image encryption algorithms by incorporating a support vector machine (SVM). In this work, we also create a dataset using standard encryption security parameters, such as entropy, contrast, homogeneity, peak signal to noise ratio, mean square error, energy, and correlation. These parameters are taken as features extracted from different cipher images. Dataset labels are divided into three categories based on their security level: strong, acceptable, and weak. To evaluate the performance of our proposed model, we have performed different analyses (f1-score, recall, precision, and accuracy), and our results demonstrate the effectiveness of this SVM-supported system

Prevalence of syphilis in Pakistani blood donors

Abstract Background: Blood transfusion is one among the common sources for transmission of the infectious diseases. In Pakistan, a country of population about 1.8 billions, blood required for transfusion is approximately 1.5 million bags per year. So, evaluation of the prevalence of syphilis among the blood donors by a retrospective study is important and critical to give a vivid picture of current situation for both the donors involved and medical practitioners.  Method: A questioner was administered and consent was taken before obtaining the blood sample for the syphilis serology from all the blood donors. ARCHITECT syphilis Treponema Pallidum (TP) assay was performed to detect the syphilis.Results: There were 449 (3.1%) confirmed cases found to be syphilis positive out of total 14,352 tested individuals. We found that male population is at far higher risk than female population. Out of 179 females, only 3 (1.6%) were found to be syphilis positive and out of 14173 males, 446 (3.1%) were having syphilis infection.Conclusions: We report high prevalence of syphilis in blood donors which was unexpected as in accordance with the previous studies. This calls for mandatory syphilis screening test of donor before transfusion of blood.  As this high prevalence poses a great risk to public health, we strongly suggest that there should me be more public awareness campaign to fight against this infectious disease