Edge-enabled Vehicular Ad Hoc Network (VANET) introduces real-time services and storage, computation, and communication facilities to the vehicles through Roadside Units (RSUs). Nevertheless, RSUs are often easy targets for security assaults due to their placement in an open, unprotected environment and resource-constrained nature. The malicious RSUs compromised by security attacks impose threats to human safety by impeding the operations of VANETs. Hence, an effective malevolent RSU detection mechanism is crucial for VANETs. Existing trust-based detection mechanisms assign trust scores to RSUs based on their interactions with moving vehicles where precise detection of rogue RSUs depends on the accuracy of trust scores. However, brief interaction of RSUs with the running vehicles permits inadequate time to estimate trust accurately. Besides, current works use only vehicle speed and density in beacon messages to assess trust without considering the sensor-detected data in the same messages. Nonetheless, sensor data is useful for traffic management, and neglecting them creates inaccuracy in trust estimation. In this paper, we address these limitations and propose a trust-based scheme to detect malicious RSUs that uses stable and frequent RSU-to-RSU (R2R) interaction to precisely analyze the behavior of an RSU. We also offer a mechanism to detect alteration of sensor-detected data in beacon content and incorporate this scheme in the trust calculation of RSUs. The experimental results show that the proposed solution effectively detects approximately 92% malicious RSUs, even in the presence of hostile vehicles. Moreover, integrating the proposed solution with the VANET routing protocols improves routing efficiency.
翻译:由安全攻击破坏的恶意区域支助股通过路边单位为车辆提供实时服务和储存、计算和通信设施。然而,由于在开放、无保护的环境和资源紧张的环境中安置,区域支助股往往很容易成为安全攻击的目标。安全攻击损害的恶意区域支助股通过阻碍VANET的运作,对人的安全构成威胁。因此,有效的恶意区域支助股检测机制对VANET至关重要。现有基于信任的检测机制根据它们与移动车辆的互动情况,向区域支助股分配了信任评分,在这些车辆与移动车辆之间,准确发现流氓区域支助股取决于信任评分的准确性。然而,区域支助股与运行车辆之间的短暂互动使得没有足够时间准确估计信任。此外,目前的工作只使用车辆速度和密度来评估信任,而没有考虑同一信息中的传感器探测数据。然而,传感器数据对交通管理有用,忽视它们造成信任估计的不准确性。在本文中,我们处理这些限制,并提议一个基于信任的方案,以检测恶意的RSU(RSU)规则的正确性分析,在不断的RSIS系统测试中,我们还可以对RSU的逻辑进行定期的计算结果。