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<p>With the rapid advancements in computing and information technologies, access control models have become increasingly essential as the first line of defense. However, many traditional methods require significant human intervention. While these rule-based approaches, crafted by experienced system engineers, are highly reliable, they are also time-consuming and dependent on human resources that may not always be available. As an alternative, the attribute-based access control model provides greater flexibility in addressing the authorization needs of complex and dynamic systems. Nevertheless, many existing approaches fail to capture the contextual meaning of attribute values, as those values are typically treated as categorical data. This paper proposes modifying our Token2Vec to handle newly added tokens without additional training. Our experiments, conducted on real-world datasets, demonstrate the effectiveness of our approach by comparing it against State-of-the-Art models and evaluating its performance in evolving system scenarios. Our new approach has proved to be effective with over 93% accuracy in all scenarios where all tokens are known, and retains its performance in scenarios where a portion of the data is not presented in the training process.</p>

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Word-embedding approach for unknown attributes in access control model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月21日

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  • Thanh Duc Bui,
  • Brajendra Panda

摘要

With the rapid advancements in computing and information technologies, access control models have become increasingly essential as the first line of defense. However, many traditional methods require significant human intervention. While these rule-based approaches, crafted by experienced system engineers, are highly reliable, they are also time-consuming and dependent on human resources that may not always be available. As an alternative, the attribute-based access control model provides greater flexibility in addressing the authorization needs of complex and dynamic systems. Nevertheless, many existing approaches fail to capture the contextual meaning of attribute values, as those values are typically treated as categorical data. This paper proposes modifying our Token2Vec to handle newly added tokens without additional training. Our experiments, conducted on real-world datasets, demonstrate the effectiveness of our approach by comparing it against State-of-the-Art models and evaluating its performance in evolving system scenarios. Our new approach has proved to be effective with over 93% accuracy in all scenarios where all tokens are known, and retains its performance in scenarios where a portion of the data is not presented in the training process.