相关实验视频
一种基于深度学习的方法,用于防止电力数据泄漏,工业物联网,商业数据交互
Weiwei Miao1, Xinjian Zhao1, Yinzhao Zhang2
1State Grid Jiangsu Electric Power Co., Ltd., Information & Telecommunication Branch, Nanjing 210024, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究引入了一种深度学习方法,以防止电力行业数据交互中的敏感数据泄露. 这种新的方法有效地识别结构化和非结构化数据中的敏感信息,增强工业物联网的安全性.
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:
- 电力行业的物联网 (IIoT) 面临着重大数据安全挑战,特别是在敏感数据交互方面.
- 目前的反泄漏策略,依赖于正则表达式,对于电力业务数据交互中的非结构化数据是不够的.
研究的目的:
- 提出一种基于深度学习的新型防泄漏方法,以确保电力业务中的数据交互.
- 确保国家电网业务平台与第三方平台之间的数据交换的安全性.
主要方法:
- 这是一种混合方法,结合了命名实体识别,正则表达式和DeBERTa-BiLSTM-CRF模型.
- 使用DeBERTa进行预训练特征提取,BiLSTM用于序列上下文语义特征提取.
- 采用CRF层,以最佳地识别敏感数据的标签序列.
主要成果:
- 拟议的方法在CLUENER 2020数据集上识别敏感数据实体时获得了81.26%的F1得分.
- 在结构化和非结构化电力业务数据中有效识别隐私敏感信息.
结论:
- 基于深度学习的方法显著提高了电力业务数据交互的安全性.
- 提供一种创新的解决方案,以减轻电力行业IIoT生态系统中数据泄露的风险.
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