声明 在物联网网络中承认访问控制政策
Li Ma1, Zexian Yang1, Zhaoxiong Bu1
1School of Electronic Information Engineering, Foshan University, Foshan 528000, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了FL-Bert-BiLSTM,这是一个新的联合学习模型,用于识别物联网网络中的访问控制策略 (ACP). 它通过使用分布式培训和预训练的词嵌入来提高准确性和数据隐私.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 访问控制政策 (ACP) 对于保护物联网 (IoT) 网络至关重要.
- 从自然语言文档中识别ACP是具有挑战性的,因为单个客户的标签数据有限.
- 现有的方法往往忽略了数据稀缺问题,阻碍了模型的准确性.
研究的目的:
- 提出一个新的模型,FL-Bert-BiLSTM,用于在物联网网络中准确和保护隐私的访问控制政策识别.
- 为了应对联邦环境中有限的标记数据的挑战.
- 利用联合学习和预先训练的词嵌入来提高ACP的认可.
主要方法:
- 开发了FL-Bert-BiLSTM,这是一个混合模型,将联合学习与BiLSTM和预先训练的词嵌入结合起来.
- 在物联网设备上实施分布式培训方法,以克服数据稀缺.
- 利用预先训练的词嵌入来捕获政策文件中的语义信息.
主要成果:
- 该FL-Bert-BiLSTM模型显著提高了ACP识别的准确性和概括能力.
- 联合学习方法有效地减轻了标记数据的稀缺性.
- 该模型在分布式培训期间展示了强大的数据隐私保护.
结论:
- 在物联网网络中,FL-Bert-BiLSTM提供了一个可行的解决方案,用于安全和高效的访问控制政策识别.
- 联合学习和预训练嵌入的组合增强了模型性能和数据隐私.
- 拟议的模型非常适合实时,物联网环境中的分布式应用程序.
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