The absence of robust security protocols renders the VANET (Vehicle ad-hoc Networks) network open to cyber threats by compromising passengers and road safety. Intrusion Detection Systems (IDS) are widely employed to detect network security threats. With vehicles' high mobility on the road and diverse environments, VANETs devise ever-changing network topologies, lack privacy and security, and have limited bandwidth efficiency. The absence of privacy precautions, End-to-End Encryption methods, and Local Data Processing systems in VANET also present many privacy and security difficulties. So, assessing whether a novel real-time processing IDS approach can be utilized for this emerging technology is crucial. The present study introduces a novel approach for intrusion detection using Federated Learning (FL) capabilities in conjunction with the BERT model for sequence classification (FL-BERT). The significance of data privacy is duly recognized. According to FL methodology, each client has its own local model and dataset. They train their models locally and then send the model's weights to the server. After aggregation, the server aggregates the weights from all clients to update a global model. After aggregation, the global model's weights are shared with the clients. This practice guarantees the secure storage of sensitive raw data on individual clients' devices, effectively protecting privacy. After conducting the federated learning procedure, we assessed our models' performance using a separate test dataset. The FL-BERT technique has yielded promising results, opening avenues for further investigation in this particular area of research. We reached the result of our approaches by comparing existing research works and found that FL-BERT is more effective for privacy and security concerns. Our results suggest that FL-BERT is a promising technique for enhancing attack detection.

翻译：缺乏健壮的安全协议使得VANET（车载自组网）网络因危及乘客和道路安全而面临网络威胁。入侵检测系统（IDS）被广泛用于检测网络安全威胁。由于车辆在道路上具有高移动性且环境多样，VANET呈现出不断变化的网络拓扑结构、缺乏隐私与安全性、带宽效率受限等问题。VANET中缺乏隐私保护措施、端到端加密方法和本地数据处理系统也带来了诸多隐私和安全难题。因此，评估能否将新型实时处理型IDS方法应用于这一新兴技术至关重要。本研究提出了一种融合联邦学习（FL）能力与BERT模型进行序列分类（FL-BERT）的新型入侵检测方法。数据隐私的重要性得到了充分认可。根据联邦学习方法，每个客户端拥有独立的本地模型和数据集。它们对模型进行本地训练，随后将模型权重发送至服务器。经过聚合后，服务器整合所有客户端的权重以更新全局模型。聚合完成后，全局模型的权重被共享给各客户端。该做法确保了敏感原始数据安全存储在各自客户端设备上，有效保护了隐私。完成联邦学习流程后，我们使用独立测试数据集评估了模型性能。FL-BERT方法取得了令人期待的结果，为该特定研究方向开辟了进一步探索的途径。我们通过对比现有研究工作得出方法结论，发现FL-BERT在隐私与安全问题方面更具有效性。我们的结果表明，FL-BERT是一种增强攻击检测性能的前景广阔的技术。