基于频道预测的安全验证,用于对事物的人工智能
Xiaoying Qiu1, Jinwei Yu2, Wenying Zhuang1
1School of Information and Management, Beijing Information Science & Technology University, Beijing 100192, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了一种用于AIoT设备的新型安全认证方案,该方案使用智能预测来检测伪造攻击. 该方法利用长短期内存 (LSTM) 和未来通道属性进行可靠的设备身份验证.
科学领域:
- 网络安全 网络安全
- 无线通信无线通信
- 机器学习 机器学习
背景情况:
- 传统的物理层非克隆性属性辅助认证 (PLUA) 方案提供可靠的指纹,但缺乏AIoT的灵活性.
- 由于分布式设备,数据复杂性和低延迟要求,AIoT应用对PLUA提出了挑战.
研究的目的:
- 为AIoT环境开发一个灵活有效的安全认证方案.
- 在动态AIoT环境中解决传统PLUA方法的局限性.
主要方法:
- 一种使用长短期内存 (LSTM) 来预测时间变化的通道信息的动态身份验证方法.
- 集成一个Savitzky-Golay过器辅助高顺序累积特征提取模型 (SGF-HOCM) 用于道数据预处理.
- 利用预测的未来道属性用于认证决策来检测克隆节点.
主要成果:
- 拟议的方案通过预测道信息,准确地检测伪造攻击.
- 在工业环境中进行广泛的实验验证实了基于预测的安全策略.
- 在识别未经授权或克隆设备方面,达到97%的高精度.
结论:
- 开发的智能预测机制增强了AIoT安全认证.
- 基于LSTM的动态身份验证与SGF-HOCM预处理为PLUA提供了灵活而准确的解决方案.
- 这种方法有效地克服了复杂的AIoT生态系统中的传统PLUA的局限性.
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