针对智能交通系统边缘节点安全，提出融合随机森林与支持向量机的入侵检测框架。
A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems
- 在边缘节点部署随机森林、决策树与线性SVM协同学习流量特征
- 通过信任感知聚合机制提升模型更新可靠性，降低误报率
- 适用于资源受限的车联网场景，适合交通网络安全研究者参考
随着智能交通系统（ITS）向更互联的边缘计算、大规模物联网及5G V2X通信演进，人工智能在边缘节点的应用对保障超低延迟通信（URLLC）和基础设施安全至关重要。然而，边缘节点分布广、异构性强且资源受限，攻击面显著扩大。本文提出一种信任感知的联邦混合入侵检测框架：各边缘节点使用随机森林、决策树与线性支持向量机（SVM）分别学习互补的流量表征；中心服务器则基于信任度评估对本地模型更新进行加权聚合，以增强整体检测鲁棒性。该方法在降低延迟与带宽开销的同时，提升了对复杂攻击的识别能力。
原文摘要 · Abstract (English)
AI-powered edge computing security is moving Intelligent Transportation Systems (ITS) from passive, rule-based protections to proactive, smart, zero-touch, self-sufficient safeguards that neutralize threats in milliseconds. As transportation becomes more connected with edge computing, massive IoT, and advanced 5G for vehicle-to-everything (V2X) connectivity, AI at the edge computing nodes plays a crucial role in protecting against sophisticated threats, enabling URLLC (ultra-low-latency communications) for smart transport, and enhancing infrastructure capabilities and safety. This research applies edge computing to improve latency, bandwidth efficiency, and service responsiveness by moving processing closer to devices, gateways, and users. However, this shift also expands the cyberattack surface because edge nodes are distributed, heterogeneous, and often resource-constrained. The paper proposes a trust-aware federated hybrid intrusion detection framework in which a random forest, a decision tree, and a linear SVM network learn complementary traffic representations at each edge site, while a server performs trust-aware aggregation of local model updates.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。