Yazar "Abdulrazzaq, M.M." seçeneğine göre listele

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    Exploring Lightweight Blockchain Solutions for Internet of Things: Review
    (Springer Science and Business Media Deutschland GmbH, 2024) Ismael, O.A.; Abdulrazzaq, M.M.; Ramaha, N.T.A.; Mukhlif, Y.A.; Al, Zakitat, M.A.S.
    The world is witnessing a major digital transformation and is moving towards more interaction, connectivity, ease, and intelligence through the Internet of Things (IoT). The IoT offers these advantages to the world by linking necessary devices with each other, making it easier to manage and deal with those devices. However, the IoT faces many challenges, such as authentication, privacy, security, and access management. The application of blockchain technology may provide a solution to these challenges. Nevertheless, applying blockchain technology may face limitations, such as the limited resources of the IoT devices used and the resource-intensive requirements of the blockchain. Therefore, to overcome these limitations, several studies have proposed using a lightweight blockchain; this blockchain is specifically designed for resource-limited IoT devices. In this paper, a comprehensive review has been made on the uses of lightweight blockchain in the IoT. Moreover, we identified some of the challenges facing the application of blockchain technologies in the IoT and the future directions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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    Harnessing Advanced Techniques for Image Steganography: Sequential and Random Encoding with Deep Learning Detection
    (Springer Science and Business Media Deutschland GmbH, 2024) Al, Zakitat, M.A.S.; Abdulrazzaq, M.M.; Ramaha, N.T.A.; Mukhlif, Y.A.; Ismael, O.A.
    This study delves into the intricacies of steganography, a method employed for concealing information within a clandestine medium to enhance data security during transmission. Given that information is often represented in various forms, such as text, audio, video, or images, steganography offers a distinctive advantage over conventional cryptography by focusing on concealing the very existence of the message, rather than merely its content. This research introduces a novel steganographic technique that places equal emphasis on both message concealment and security enhancement. This study highlights two primary steganographic methods: sequential encoding and random encoding. By employing both encryption and image compression, these techniques fortify data security while preserving the visual integrity of cover images. Advanced deep learning models, namely Vgg-16 and Vgg-19, are proposed for the detection of image steganography, with their accuracy and loss rates rigorously evaluated. The significance of steganography extends across various sectors, including the military, government, and online domains, underscoring its pivotal role in contemporary data communication and security. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.