高精度的自适应联合森林基于对抗攻击的抵抗力在无线交通预测中的无线交通预测
Lingyao Wang1, Chenyue Pan2, Haitao Zhao1
1College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
超越5G网络需要可靠的流量预测. 新的自适应值修改联合森林 (ATMFF) 模型有效识别对抗性攻击,增强智能通信系统的预测安全性和性能.
科学领域:
- 电信工程 电信工程 电信工程
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 目前的5G网络面临着局限性,需要开发超越5G (B5G) 扩展通信能力.
- B5G需要智能流量预测,以实现高效的网络管理,资源配置和性能提升.
- 联合学习对于保护隐私的交通预测至关重要,但易受对手攻击的影响.
研究的目的:
- 引入一种新的方法来保护B5G网络中的基于联合学习的流量预测,防止对抗性攻击.
- 提高智能交通预测系统的可靠性和准确性.
主要方法:
- 适应值修改联邦森林 (ATMFF) 模型的开发.
- 使用基于混矩阵率的选加权聚合实现适应性值修改.
- 使用现实世界5G流量数据进行评估.
主要成果:
- 与传统的多重提升模型相比,ATMFF显示出更高的对抗性样本识别准确性.
- 拟议的方法显著提高了交通预测模型的安全性和可靠性.
- ATMFF提高了识别对抗样本的准确性,确保了预测系统的完整性.
结论:
- ATMFF有效地保护B5G流量预测的联合学习,防止对抗性威胁.
- 该模型为智能交通分类服务提供了可靠的解决方案.
- 这一进步对于未来通信网络的安全和高效运行至关重要.
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