Special Issues
Table of Content

Emerging Technologies in Information Security

Submission Deadline: 31 January 2025 View: 263 Submit to Special Issue

Guest Editors

Dr Jawad Ahmad, Edinburgh Napier University, UK
Dr Mujeeb Ur Rehman, De Montfort University, UK
Dr Wadii Boulila, Prince Sultan University, Kingdom of Saudi Arabia

Summary

Information security is one of the fastest-growing and most diverse areas due to recent advancements in digital technologies and increasingly strict security and data protection regulations. As a result, public sector and private organizations have been compelled to take their cybersecurity seriously, implementing more robust measures and policies against cyber threats. To protect private and sensitive data, state-of-the-art encryption methods are available; however, previous schemes are either impractical or insecure. For example, the Advanced Encryption Standard (AES) is well-suited for text encryption, and lightweight solutions should be proposed for image encryption applications. Thus, there is a need for proposing lightweight image encryption schemes for real-time practical applications. Similarly, in other domains, researchers and organizations should come up with novel solutions that address the unique challenges and requirements to ensure information security is both effective and efficient. This special issue provides a valuable platform to showcase high-quality contributions to information security. The primary aim is to focus on all aspects of emerging technologies in information security to highlight future research directions. Therefore, we invite academics and industry professionals to submit original research articles and review articles that focus on exploring novel ideas in the area of information security.


Keywords

Data Privacy, Malware Analysis, Cloud Security, Intrusion Detection, Threat Intelligence, IoT Security, AI in Security, Biometric Security, Risk Assessment, Blockchain, Mobile Security, Multimedia Encryption

Published Papers


  • Open Access

    ARTICLE

    Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain

    Zengxiang Li, Yongchong Wu, Alanoud Al Mazroa, Donghua Jiang, Jianhua Wu, Xishun Zhu
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.051762
    (This article belongs to the Special Issue: Emerging Technologies in Information Security )
    Abstract Hidden capacity, concealment, security, and robustness are essential indicators of hiding algorithms. Currently, hiding algorithms tend to focus on algorithmic capacity, concealment, and security but often overlook the robustness of the algorithms. In practical applications, the container can suffer from damage caused by noise, cropping, and other attacks during transmission, resulting in challenging or even impossible complete recovery of the secret image. An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms. In this proposed algorithm, a secret… More >

  • Open Access

    ARTICLE

    Anomaly-Based Intrusion Detection Model Using Deep Learning for IoT Networks

    Muaadh A. Alsoufi, Maheyzah Md Siraj, Fuad A. Ghaleb, Muna Al-Razgan, Mahfoudh Saeed Al-Asaly, Taha Alfakih, Faisal Saeed
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.052112
    (This article belongs to the Special Issue: Emerging Technologies in Information Security )
    Abstract The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated… More >

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