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 Articles will not be published until the final proofs are validated by their authors.

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

Regular Issues

  • Tomato Disease Classification and Recognition using Machine Learning   Order a copy of this article
    by Bingjie Liu, Vladimir Y. Mariano 
    Abstract: Tomatoes are one of the primary vegetable foods in human society, yet they are susceptible to various diseases. This study used neural network algorithms to design an image recognition system for tomato leaf diseases. Based on the PlantVillage tomato dataset, the image data were pre-processed and normalised, and training and testing sets were selected and sent to three models: Convolutional Neural Network (CNN), Residual Network (ResNet), and Visual Geometry Group (VGG), for comparative training analysis. The experiment employed the cross-entropy function to balance sample differences, and the model training process was enhanced through reasonable parameter settings. Furthermore, the deployment challenges and solutions of the experimental model in real-world scenarios were discussed. After training, the VGG model performed the best, achieving a recognition accuracy rate of 84% and a precision rate of 83.5%.
    Keywords: Tomato disease classification; CNN; VGG; cross entropy.
    DOI: 10.1504/IJSN.2024.10069604
     
  • Enable Membership Privacy-Preserving Group Signature Scheme   Order a copy of this article
    by Zhihong Chen, Huiying Hou, Zhenyu Liu, Zhiyuan Ren, Yucong Ma 
    Abstract: Group members can generate an unalterable signature for messages representing the group through group signatures. Conventional group signatures fail to acknowledge that the list of group members is sensitive data by nature. Fully dynamic group signature methods with member privacy have been developed to solve this problem. However, these methods cannot identify instances where the same group member has signed the same message twice since they do not offer message linkability. Signing the same message twice might harm users interests in specific applications, such as blockchain-based credit and punishment systems. This work introduces a completely flexible group signature system that enhances the privacy of its members while ensuring the traceability of messages alongside a feasible application. Under our method, only the group administrator can recognise if two signatures originate from the same individual within the group, while the list of group members remains completely concealed. Additionally, the group administrator can ascertain the actual identity of the group member. Additionally, any user can confirm whether two distinct signatures for the same communication are from the same signer. Lastly, we show our approachs efficiency and security through several simulation tests and formal security analysis.
    Keywords: fully dynamic group signatures; membership privacy; message linkability; signatures on equivalence classes; blockchain security.
    DOI: 10.1504/IJSN.2025.10069676
     

 

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