积极和隐私 - 通过联合AI认证 (PAFA-DoH) 通过HTTPS对DNS的保护
1State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, 210093, Jiangsu, China.
概括
PAFA-DoH通过统一联合人工智能,量子弹性加密和神经形态异常检测来增强DNS与HTTPS的隐私. 这种新的框架在检测恶意DNS活动时达到99.20%以上的准确性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 密码学 密码学 密码学 密码学
背景情况:
- 通过HTTPS (DoH) 的DNS提供了隐私,但仍然容易受到道挖掘,解析器操纵和侧通道泄漏的影响.
- 现有的防御系统难以应对加密DNS流量的复杂威胁.
研究的目的:
- 引入PAFA-DoH,这是一个统一的框架,用于保护隐私,对加密DNS进行后量子硬化保护.
- 开发一个能够检测和减轻高级威胁而不损害用户隐私的系统.
主要方法:
- 利用对抗式联合学习与同态推理用于加密流量的协作模型改进.
- 在神经形态硬件上使用拓数据分析和尖端神经网络 (SNN) 进行实时异常检测.
- 整合了人工智能辅助的零知识认证 (ZK-SNARKs/STARKs) 以确保可验证的客户端和解决方案完整性.
主要成果:
- 在检测恶意DNS活动时实现了≥99.20%的准确性.
- 证明零知识 (ZK) 验证 (<100ms) 和握手 (<150ms) 的低延迟.
- 联合学习模式在10个时代内融合,展示了高效的培训.
结论:
- PAFA-DoH提供了一个实用,可部署的解决方案,用于零信任,后量子硬化DNS保护.
- 保护隐私的人工智能,可验证的信任和高效的分析的结合解决了加密DNS中的关键安全漏洞.
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