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The advent of blockchain technology within the healthcare domain has signified a paradigm shift, transitioning from an emerging trend to an essential infrastructure component that ensures decentralization, transparency, integrity, and persistent availability. Despite its potential, the healthcare sector has not fully capitalized on the vast array of benefits blockchain technology offers. Most existing works utilized blockchain technology within a specific healthcare entity's services but not among several healthcare organizations. They notably lack the provision for direct communication and knowledge transfer between doctors from different hospitals. Therefore, this paper introduces a pioneering Blockchain-based Unified Medical Consultancy Service (B-UMCS) that leverages blockchain's robustness to revolutionize telehealth services by (a) alleviating the shortage of medical expertise through facilitating the interconnection of physicians from diverse hospitals and geographical areas onto a consolidated platform, (b) promoting the seamless sharing of medical consultations, electronic health records (EHRs), and expert insights while upholding rigorous security and privacy protocols, (c) integrating the inherent security mechanisms of blockchain with the distributed data storage functionality offered by the Interplanetary File System (IPFS). This work details the B-UMCS's components, interactions, smart contracts, protocols, algorithms, storage and transmission of EHRs, and their corresponding implementations. The evaluations of the proposed B-UMCS reveal that it secures and facilitates the sharing of EHRs and enables healthcare professionals to collaborate and exchange expertise seamlessly across institutional boundaries. They are additionally ensuring that healthcare providers can offer their knowledge in an efficient and scalable manner. Overall, B-UMCS not only addresses the current challenges in healthcare data security and accessibility but also opens new avenues for collaboration and knowledge sharing among healthcare professionals, ultimately contributing to improving patient care quality. [ABSTRACT FROM AUTHOR]

This paper presents a robust multi-stage security solution based on fusion, encryption, and watermarking processes to transmit color healthcare images, efficiently. The presented solution depends on the features of discrete cosine transform (DCT), lifting wavelet transform (LWT), and singular value decomposition (SVD). The primary objective of this proposed solution is to ensure robustness for the color medical watermarked images against transmission attacks. During watermark embedding, the host color medical image is transformed into four sub-bands by employing three stages of LWT. The resulting low-frequency sub-band is then transformed by employing three stages of DCT followed by SVD operation. Furthermore, a fusion process is used for combining different watermarks into a single watermark image. This single fused image is then ciphered using Deoxyribose Nucleic Acid (DNA) encryption to strengthen the security. Then, the DNA-ciphered fused watermark is embedded in the host medical image by applying the suggested watermarking technique to obtain the watermarked image. The main contribution of this work is embedding multiple watermarks to prevent identity theft. In the presence of different multimedia attacks, several simulation tests on different colormedical images have been performed. The results prove that the proposed security solution achieves a decent imperceptibility quality with high Peak Signal-to-Noise Ratio (PSNR) values and high correlation between the extracted and original watermark images. Moreover, the watermark image extraction process succeeds in achieving high efficiency in the presence of attacks compared with related works. [ABSTRACT FROM AUTHOR]

The continuous digitization of healthcare services makes them more targeted by security attackers that attempt to steal the patients' confidential records and hijack their healthcare rights. Consequently, many existing approaches were proposed to protect healthcare data and services. However, these current solutions lack efficiency, as indicated by the high number of security breaches in healthcare systems. Therefore, this research was motivated to introduce a more efficient algorithm that achieves several essential security requirements such as authentication, confidentiality, and integrity while preserving high resistance against a comprehensive set of different security threats. This proposed algorithm is a hybrid optical-based that utilizes effective hashing, steganography, and encryption techniques for the secure transmission of color or grayscale medical images, even over insecure channels. The input medical image is initially decomposed into three color components (red, green, and blue). Then, each one of these color components is forwarded to multiple sequential security stages. At the first security stage, the Discrete Wavelet Transform (DWT)-based compressive sensing technique is employed to compress the color components of the plaintext medical image to obtain the compressed image components. After that, the sigmoid function-based quantization process is applied to the compressed image components to generate the digital quantized image components. The digital pixels of these components will then be encrypted using Rubik's cube-based encryption algorithm to obtain the final ciphertext medical image. In parallel, to ensure the authentication and integrity of the transmitted medical image, the image phase component is extracted using the optical Double Random Phase Encoding (DRPE) technique. Then, it is quantized before concatenation with a secret key and forwarded to the SHA-256 hashing algorithm to generate the HMAC digest (Hash-based Message Authentication Code value). The HMAC digest is then embedded using Least Significant Bit (LSB)-based steganography within the final encrypted color medical image to increase its secrecy. The proposed algorithm was extensively evaluated using different quality and security assessment metrics to validate its robustness and efficiency against various channel noise and attacks. The obtained outcomes prove the higher performance of the proposed algorithm for secure medical image communication than other recent and related security algorithms in terms of all tested computational and security parameters. In addition, the obtained results confirm that the optical-based security techniques have superior efficiency & robustness and low complexity compared to the related traditional digital-based security techniques. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is prone to malware assaults due to its simple installation and autonomous operating qualities. IoT devices have become the most tempting targets of malware due to well-known vulnerabilities such as weak, guessable, or hard-coded passwords, a lack of secure update procedures, and unsecured network connections. Traditional static IoT malware detection and analysis methods have been shown to be unsatisfactory solutions to understanding IoT malware behavior for mitigation and prevention. Deep learning models have made huge strides in the realm of cybersecurity in recent years, thanks to their tremendous data mining, learning, and expression capabilities, thus easing the burden on malware analysts. In this context, a novel detection and multi-classification vision-based approach for IoT-malware is proposed. This approach makes use of the benefits of deep transfer learning methodology and incorporates the fine-tuning method and various ensembling strategies to increase detection and classification performance without having to develop the training models from scratch. It adopts the fusion of 3 CNNs, ResNet18, MobileNetV2, and DenseNet161, by using the random forest voting strategy. Experiments are carried out using a publicly available dataset, MaleVis, to assess and validate the suggested approach. MaleVis contains 14,226 RGB converted images representing 25 malware classes and one benign class. The obtained findings show that our suggested approach outperforms the existing state-of-the-art solutions in terms of detection and classification performance; it achieves a precision of 98.74%, recall of 98.67%, a specificity of 98.79%, F1-score of 98.70%, MCC of 98.65%, an accuracy of 98.68%, and an average processing time per malware classification of 672 ms. [ABSTRACT FROM AUTHOR]