Vehicle Ad Hoc Networks (VANETs) are considered to be a next big thing that will remarkably change our lives, since this kind of technology is able to make our lives and roads safer. Intrusion Detection Systems (IDS) is an important technology that can mitigate both inner and outer threats for the vulnerable networks like VANETs. However, it is difficult to adopt the same IDSs that have been appropriately used in wired networks, due to the fast moving and highly dynamic nature of VANETs. Thus, in this article, an Efficient and Collaborative Framework with a Markov-based Reputation Scheme is proposed, namely ECF-MRS. In the proposed framework, the collaborative mechanism is achieved by using Non-dominant Sorting Genetic Algorithm-III (NSGA-III)-Collaboration to merge the advantages of IDSs in VANETs to generate a more superior IDS, while Non-Linear Programming (NLP)-Optimization is designed as the efficient mechanism to reduce the execution time of IDSs in VANETs. Moreover, considering the security risks of collaboration, a Reputation Scheme based on the Hidden Generalized Mixture Transition Distribution (HgMTD) model, namely RS-HgMTD, is proposed for each vehicle in VANETs to evaluate the creditworthiness of their neighbors. Experiments show that the IDS with ECF-MRS has better performance than other existing IDSs in terms of detection rate, detection time and overhead.

中文翻译：

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ECF-MRS：一种基于马尔可夫信誉方案的高效协作框架，适用于车辆网络中的 IDS


车辆自组织网络（VANET）被认为是下一个将显着改变我们生活的重大事件，因为这种技术能够使我们的生活和道路更安全。入侵检测系统 (IDS) 是一项重要技术，可以减轻 VANET 等易受攻击网络的内部和外部威胁。然而，由于 VANET 的快速移动和高度动态特性，很难采用与有线网络中适当使用的相同的 IDS。因此，本文提出了一种基于马尔可夫信誉方案的高效协作框架，即ECF-MRS。在所提出的框架中，协作机制是通过使用非支配排序遗传算法-III（NSGA-III）-协作来融合VANET中IDS的优点以生成更优越的IDS，同时非线性编程（NLP） -优化被设计为减少 VANET 中 IDS 执行时间的有效机制。此外，考虑到协作的安全风险，针对VANET中的每辆车提出了一种基于隐广义混合转移分布（HgMTD）模型的信誉方案，即RS-HgMTD，以评估其邻居的信誉度。实验表明，采用ECF-MRS的IDS在检测率、检测时间和开销方面都比现有的其他IDS具有更好的性能。