Digital twin technology is crucial to the development of the sixth-generation (6G) Internet of Vehicles (IoV) as it allows the monitoring and assessment of the dynamic and complicated vehicular environment. However, 6G IoV networks have critical challenges in network security and computational efficiency, which need to be addressed. Existing digital twin technologies in 6G IoV networks often suffer from limitations, such as reliance on static models and high computational demands, leading to unstable attack detection and inefficiencies. Their results for attack detection performance metrics, precision, detection rate, and F1-Score are insufficient for 6G IoV. Moreover, these systems concentrate all computational processes within the digital twin’s service layer, leading to inefficiencies. To address these challenges, we introduce a novel artificial intelligence (AI) enhanced digital twin framework designed to significantly improve 6G IoV network security and computational efficiency under dynamic conditions. Our framework employs an advanced feature engineering module that uses feature selection methods and stacked sparse autoencoders (ssAE) to reduce feature dimensions within the cyber twin layer, effectively distributing the overall computational load. It also utilizes an online learning module which enables a network-aware attack detection mechanism for precise attack detection. The proposed solution exhibits a stable performance of around 98% success rate regarding attack detection metrics against two data sets. Specifically, our solution reduces system latency by 12%, energy consumption by 15%, RAM usage by 20%, and improves packet delivery rates by 6.1%. These findings underscore the potential of our framework to enhance the robustness and responsiveness of 6G IoV systems, offering a significant contribution to vehicular network security and management.

中文翻译：

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用于网络弹性 6G 车联网的 AI 增强型数字孪生框架


数字孪生技术对于第六代 （6G） 车联网 （IoV） 的发展至关重要，因为它可以监控和评估动态和复杂的车辆环境。然而，6G 车联网网络在网络安全和计算效率方面存在重大挑战，亟待解决。6G 车联网网络中现有的数字孪生技术往往存在依赖静态模型、计算需求高等局限性，导致攻击检测不稳定、效率低下。他们的攻击检测性能指标、精度、检测率和 F1-Score 的结果不足以用于 6G 车联网。此外，这些系统将所有计算过程集中在数字孪生的服务层中，导致效率低下。为了应对这些挑战，我们引入了一种新颖的人工智能 （AI） 增强数字孪生框架，旨在显著提高动态条件下 6G 车联网的安全性和计算效率。我们的框架采用先进的特征工程模块，该模块使用特征选择方法和堆叠稀疏自动编码器 （ssAE） 来减少网络孪生层内的特征维度，从而有效地分配整体计算负载。它还利用在线学习模块，该模块支持网络感知攻击检测机制，以实现精确的攻击检测。所提出的解决方案在针对两个数据集的攻击检测指标方面表现出约 98% 的成功率的稳定性能。具体来说，我们的解决方案将系统延迟降低了 12%，能耗降低了 15%，RAM 使用率降低了 20%，数据包送达率提高了 6.1%。 这些发现强调了我们的框架在增强 6G 车联网系统的稳健性和响应能力方面的潜力，为车辆网络安全和管理做出了重大贡献。