概括
本研究介绍了一种新的自适应式,端到端 (E2E) 安全传输方法,用于无线电光纤 (RoF) 系统. 它使用深度学习来增强对各种攻击的物理层安全性.
科学领域:
- 电信工程 电信工程 电信工程
- 网络安全 网络安全
- 光学通信是指光学通信.
背景情况:
- 无线电在光纤 (RoF) 系统对于高速通信网络至关重要.
- 确保RoF系统中的物理层安全性,以防止复杂的攻击,仍然是一个挑战.
- 集成深度学习等先进技术可能会提高RoF系统的安全性.
研究的目的:
- 为RoF系统提出一种新的自适应,端到端 (E2E) 安全传输方法.
- 将加密功能直接嵌入到调制和解调制过程中.
- 增强物理层安全性,防止白盒和灰盒攻击.
主要方法:
- 开发了一个E2E传输框架,集成深度学习 (DL) 和传统模型.
- 通过E2E优化嵌入加密到调制 (TransNN) 和解调 (ReceivNN).
- 员工培训阶段随机化,噪音干扰和对抗性安全培训.
主要成果:
- 证明只有匹配的ReceivNN才能在白盒攻击下正确地调节TransNN信号.
- 由于频道条件不匹配,在灰盒攻击下显示了ReceivNN的性能下降.
- 验证了该计划对白盒和灰盒对抗性攻击的有效性.
结论:
- 拟议的E2E安全传输方法有效地提高了RoF系统中的物理层安全性.
- 该系统的自适应性为各种窃听尝试提供了强大的保护.
- 这种方法为未来的RoF通信网络提供了有希望的安全和适应性解决方案.
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