Forthcoming and Online First Articles

International Journal of Security and Networks

International Journal of Security and Networks (IJSN)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

International Journal of Security and Networks (2 papers in press)

Regular Issues

  • Information Security Based on Featureless Attack Algorithm   Order a copy of this article
    by Huiru Zhang, Ruixiao Liu, Huijuan Liu 
    Abstract: This study proposes a model for generating and recovering adversarial samples to address the issue of machine learning systems being vulnerable to attacks. The model includes a featureless attack algorithm based on generative adversarial networks and an adversarial sample generation model. The performance of the machine learning system in the face of attacks is evaluated using this model. To address the issue of poor defence against data processing-based adversarial samples, a convolutional neural network-based adversarial sample recovery model is then built to improve the detection and response capability of machine learning systems facing adversarial attacks. The results indicated that under the attack of the featureless attack algorithm, the accuracy of each classifier gradually decreases, and finally lower than 0.1. The bypass rate of the featureless attack algorithm was high, and the probability of the classifier recognising the antagonistic samples was up to 4%. The convolutional neural network-based antagonistic samples recovery model had better image denoising effect and defence effect. In summary, the model constructed by the research has a good application effect, which helps to improve the antagonistic defence ability of the machine learning system and guarantee the information security.
    Keywords: information security; generative adversarial networks; adversarial samples; convolutional neural networks; CNNs.
    DOI: 10.1504/IJSN.2024.10065630
     
  • Resource Allocation for Wireless Networks based on Enhanced Harris Hawks Optimisation Algorithm   Order a copy of this article
    by Xianmeng Meng, Cuicui Cai, Linglong Tan, Qijian Wang 
    Abstract: Efficient resource allocation is critical to improve the quality of service in wireless networks. The problem of resource allocation is usually nonconvex and nondeterministic polynomial-hard. Meta-heuristic algorithms are widely used to solve nonconvex optimization problems due to their efficient execution and high iteration accuracy. The Harris Hawk optimisation (HHO) algorithm has recently received attention as an effective approach for addressing various optimisation problems. However, the HHO algorithm is employed for resource allocation issues in WNs and suffers from early convergence and local optimal problems. To overcome these issues, we develop a novel enhanced HHO (EHHO) algorithm with low computational complexity and explore the performance of the EHHO in resource allocation. Firstly, we introduce the power allocation for secrecy rate maximisation and spectrum efficiency trade-off in wireless networks.
    Keywords: resource allocation; meta-heuristic optimization; enhanced Harris Hawks optimization algorithm; secrecy rate maximization; energy-spectral efficiency tradeoff.
    DOI: 10.1504/IJSN.2024.10067901
     

 

Return to top