Software defined Internet of Vehicles (SD-IoV) is an emerging paradigm for accomplishing Industrial Internet of Things (IIoT). Unfortunately, SD-IoV still faces security challenges. Traditional solutions respond after attacks happening, which is low-effective. To cope with this problem, moving target defense (MTD) was proposed to modify network configurations dynamically. However, current MTD for IIoT has several drawbacks: 1) it cannot handle highly dynamic environments; 2) MTD strategy lacks intelligence because it needs attack–defense models; 3) they are difficult to trace sources. In this article, we propose an intelligent MTD scheme to defend against distributed denial-of-service in SD-IoV. Firstly, we model the configuration mutation of roadside units as a Markov decision process (MDP), and adopt deep reinforcement learning to solve the optimal configuration. Next, we evaluate the trust of vehicles after shuffling, which can distinguish spy vehicles. Finally, extensive simulation results confirm the effectiveness of our solution compared with representative methods.

中文翻译：

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如何在 SD-IoV 中智能缓解 DDoS：移动目标防御方法


软件定义的车联网 (SD-IoV) 是实现工业物联网 (IIoT) 的新兴范例。不幸的是，SD-IoV仍然面临安全挑战。传统的解决方案是在攻击发生后才进行响应，效率较低。为了解决这个问题，移动目标防御（MTD）被提出来动态修改网络配置。然而，当前 IIoT 的 MTD 有几个缺点：1）它无法处理高度动态的环境； 2）MTD策略缺乏智能，需要攻防模型； 3）来源难以追踪。在本文中，我们提出了一种智能 MTD 方案来防御 SD-IoV 中的分布式拒绝服务。首先，我们将路边单元的配置突变建模为马尔可夫决策过程（MDP），并采用深度强化学习来求解最优配置。接下来，我们评估洗牌后车辆的信任度，这可以区分间谍车辆。最后，广泛的模拟结果证实了我们的解决方案与代表性方法相比的有效性。