模拟恶意交通模式对车联网通信的影响，发现可导致96.55%的报文丢失。
Evaluating the impact of adversarial traffic patterns on vanet communication using veins simulation
- 用Veins框架设计多种恶意攻击场景，模拟车辆间通信干扰。
- 低密度下消息泛洪使吞吐量下降27.89%，最高致报文丢包率达96.55%。
- 适合研究车联网安全与鲁棒性设计的学者和工程师参考。
车载自组织网络（VANETs）是智能交通系统的关键组成部分，支持车辆间实时通信。然而其开放和动态特性使其极易受到恶意行为影响，导致通信可靠性下降。本文利用集成OMNeT++与SUMO的Veins仿真框架，研究了不同交通密度和移动条件下，多种恶意交通模式对VANET性能的影响。设计并评估了消息泛洪、虚假信息传播及协同拥塞攻击等场景，测量报文交付率（PDR）、端到端延迟和网络吞吐量等关键指标。实验结果表明，恶意攻击可使PDR降低高达96.55%，在低密度环境下消息泛洪导致吞吐量下降27.89%，显著降低网络整体效率。研究揭示了车联网通信中的关键脆弱点，为构建更鲁棒、安全的车载网络提供了重要依据。
原文摘要 · Abstract (English)
Vehicular Ad Hoc Networks (VANETs) are a key component of intelligent transportation systems, enabling real-time communication between vehicles. However, their open and dynamic nature makes them highly vulnerable to adversarial behaviors that can disrupt communication reliability. This paper investigates the impact of adversarial traffic patterns on VANET performance using the Veins simulation framework integrated with OMNeT++ and SUMO. We design and evaluate multiple adversarial scenarios, including message flooding, false information dissemination, and coordinated congestion attacks, under varying traffic densities and mobility conditions. The study measures key performance metrics such as packet delivery ratio (PDR), end-to-end delay, and network throughput. Experimental results show that adversarial traffic can reduce PDR by up to 96.55%, with message flooding at low density producing a throughput reduction of 27.89%, and significantly degrade overall network efficiency. The findings highlight critical vulnerabilities in VANET communication and provide insights into designing more resilient and secure vehicular networks.
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