Inherent unreliability of wireless communications may have crucial consequences when safety-critical C-ITS applications enabled by VANETs are concerned. Although natural sources of packet losses in VANETs such as network traffic congestion are handled by decentralized congestion control (DCC), losses caused by malicious interference need to be controlled too. For example, jamming DoS attacks on CAMs may endanger vehicular safety, and first and foremost are to be detected in real time. Our first goal is to discuss key literature on jamming modeling in VANETs and revisit some existing detection methods. Our second goal is to present and evaluate our own recent results on how to address the real-time jamming detection problem in V2X safety-critical scenarios with the use of AI. We conclude that our hybrid jamming detector, which combines statistical network traffic analysis with data mining methods, allows the achievement of acceptable performance even when random jitter accompanies the generation of CAMs, which complicates the analysis of the reasons for their losses in VANETs. The use case of the study is a challenging platooning C-ITS application, where V2X-enabled vehicles move together at highway speeds with short inter-vehicle gaps.

中文翻译：

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VANET中基于AI的恶意网络流量检测

当涉及到由VANET启用的对安全至关重要的C-ITS应用程序时，无线通信固有的不可靠性可能会产生严重的后果。尽管VANET中数据包丢失的自然原因（例如网络流量拥塞）是通过分散的拥塞控制（DCC）处理的，但也需要控制恶意干扰导致的丢失。例如，对CAM进行DoS攻击可能会危害车辆安全，并且首先要实时检测。我们的首要目标是讨论VANET中干扰建模的关键文献，并重新研究一些现有的检测方法。我们的第二个目标是展示和评估我们自己最近的结果，该结果涉及如何使用AI解决V2X安全关键场景中的实时干扰检测问题。我们得出结论，我们的混合干扰检测器 它结合了统计网络流量分析和数据挖掘方法，即使在随机抖动伴随CAM生成的情况下，也可以实现可接受的性能，这使得分析其在VANET中丢失的原因变得更加复杂。该研究的用例是具有挑战性的C-ITS排应用程序，其中启用V2X的车辆以较短的车距在高速公路上行驶。