论文标题
GFCL：一个基于GRU的联邦持续学习框架，反对IOV中的数据中毒攻击
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV
论文作者
论文摘要
基于5G的车辆互联网（IOV）网络中机器学习（ML）的集成使智能运输和智能流量管理。尽管如此，抵抗对抗中毒攻击的安全也越来越成为一项艰巨的任务。具体而言，深钢筋学习（DRL）是IOV应用中广泛使用的ML设计之一。标准的ML安全技术在DRL中无效，该算法学会通过与环境的持续互动来解决顺序决策，并且环境是时间变化，动态和移动性的。在本文中，我们提出了一个基于IOV中基于SYBIL的数据中毒攻击的封闭式复发单元（GRU）联合持续学习（GFCL）异常检测框架。目的是提出一个轻巧且可扩展的框架，该框架在不包含攻击样本的A-Priori培训数据集的情况下学习和检测非法行为。我们使用GRU预测未来的数据顺序，以基于联合学习的分布方式分析和检测车辆的非法行为。我们使用现实世界的车辆移动轨迹研究了框架的性能。结果证明了我们提出的解决方案在不同性能指标方面的有效性。
Integration of machine learning (ML) in 5G-based Internet of Vehicles (IoV) networks has enabled intelligent transportation and smart traffic management. Nonetheless, the security against adversarial poisoning attacks is also increasingly becoming a challenging task. Specifically, Deep Reinforcement Learning (DRL) is one of the widely used ML designs in IoV applications. The standard ML security techniques are not effective in DRL where the algorithm learns to solve sequential decision-making through continuous interaction with the environment, and the environment is time-varying, dynamic, and mobile. In this paper, we propose a Gated Recurrent Unit (GRU)-based federated continual learning (GFCL) anomaly detection framework against Sybil-based data poisoning attacks in IoV. The objective is to present a lightweight and scalable framework that learns and detects the illegitimate behavior without having a-priori training dataset consisting of attack samples. We use GRU to predict a future data sequence to analyze and detect illegitimate behavior from vehicles in a federated learning-based distributed manner. We investigate the performance of our framework using real-world vehicle mobility traces. The results demonstrate the effectiveness of our proposed solution in terms of different performance metrics.
