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在线数据中毒攻击对边缘AI范式进行IoT启用智慧城市攻击
Yanxu Zhu1,2,3, Hong Wen1,2,3, Jinsong Wu4,5
1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
本研究介绍了智能城市边缘AI的在线中毒攻击框架,增强了对数据操纵的安全性. 这种新的方法有效地针对使用有限资源的AI模型,优于现有方法.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 物联网的物联网,就是物联网.
背景情况:
- 智慧城市的边缘人工智能整合了边缘计算和人工智能,创造了新的漏洞.
- 诸如数据中毒,模型中毒和逃避等攻击对这些系统构成重大威胁.
- 边缘环境中的有限存储和动态数据流使防御策略复杂化.
研究的目的:
- 建议在线中毒攻击框架,适用于物联网智能城市的边缘人工智能.
- 解决边缘环境中有限的存储和动态数据流的挑战.
- 开发有效的毒药策略,克服现有在线攻击的局限性.
主要方法:
- 一个基于排练的缓冲机制,用于缓存中的增量数据流操纵.
- 使用在线代计算的基于最大梯度的样本选择策略.
- 一个以最大损失为基础的样本污染策略,以使每个中毒样本能够进行多次更新.
- 两级优化问题的转换从离线到在线模式.
主要成果:
- 拟议的在线灰色盒中毒攻击算法在边缘设备上实施和评估.
- 模拟使用了从离线开放网格数据集生成的在线数据流.
- 与基线方法相比,开发的方法显示出更高的攻击效率.
- 这种方法还显示出在开销方面提高了效率.
结论:
- 拟议的框架有效地在资源有限的边缘人工智能环境中执行在线中毒攻击.
- 新的策略克服了传统在线中毒攻击的关键局限性.
- 该方法提供了一种实用和有效的方法来评估支持物联网的智能城市系统的安全性.
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