使用DCTF和深度学习的5G移动设备的射频指纹识别
Hua Fu1,2, Hao Dong1, Jian Yin1
1School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China.
Entropy (Basel, Switzerland)
|January 22, 2024
概括
无线电频率指纹 (RFF) 识别通过检测伪造来提高5G安全性. 新的DCTF方案实现了超过92%的准确性来验证5G手机的真实性.
科学领域:
- 网络安全 网络安全
- 无线通信无线通信
- 信号处理 信号处理
背景情况:
- 第五代 (5G) 网络面临着重大安全漏洞.
- 使用射频指纹 (RFF) 的物理层认证提供了一个有希望的防御.
- RFF可以检测到复杂的攻击,如伪造和分布式拒绝服务 (DDoS).
研究的目的:
- 为了评估5G手机RFF识别的性能.
- 为增强安全性提出基于DCTF的新型RFF识别方案.
- 在不同的条件下评估这些方案的准确性.
主要方法:
- 从物理随机访问通道 (PRACH) 序言中提取差分星座轨迹图 (DCTF).
- 开发基于索引的DCTF识别方案,使用完整的PRACH序言数据库.
- 针对部分序言培训的场景,提出基于组的DCTF识别方案.
- 实验验证使用三个型号的六部5G手机和基于OpenAirInterface的5G gNB.
主要成果:
- 基于索引的DCTF识别方案在25dB的信号噪声比率下实现了92.78%的分类准确性.
- 基于集团的DCTF识别方案显示了89.59%的分类准确性.
- 实验设置证实了拟议方法的实际可行性.
结论:
- 基于DCTF的RFF识别对于5G移动设备的认证是有效的.
- 拟议的基于索引和基于组的方案为5G物理层安全提供了强大的解决方案.
- 这些发现有助于保护不断发展的5G生态系统免受新出现的威胁.
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