Malware propagation in IoT (Internet of Things) systems can lead to data leakages, financial losses, and other serious consequences. To solve this issue, we propose a new active IoT malware propagation defence work. Specifically, aided by stochastic games, we express the process of cyber conflicts between IoT system nodes and edge devices considering malware propagation in edge intelligence-enabled IoT. Here, IoT system nodes and edge devices choose their own strategies and receive the corresponding rewards determined by the current state and strategy. After that, the game randomly moves to the next stage according to the distribution of probabilities and the participants’ strategies until reaching the fixed Nash equilibrium point. Following a theoretical analysis, we design and implement SGD3QN (Stochastic Games and Dueling Double Deep Q-networks)—a novel algorithm to receive the optimal strategy for mitigating IoT malware propagataion in practice. Here, the Dueling Double Deep Q-networks are acted as an end-to-end decision control system, in which IoT malware propagataion environment is used as the input to obtain the failure or success experience to update the network parameters, followed by making the optimal decision output. Afterwards, we perform experimental simulations that probe the influence of batch size and replay memory size on the optimal IoT malware propagation defense strategy selection and prove the ascendancy of the proposed SGD3QN-aided decision-making algorithm.

中文翻译：

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SGD3QN：联合随机博弈和双深度 Q 网络决斗，用于防御边缘智能物联网中的恶意软件传播


IoT（物联网）系统中的恶意软件传播可能导致数据泄露、财务损失和其他严重后果。为了解决这个问题，我们提出了一种新的主​​动物联网恶意软件传播防御工作。具体来说，在随机博弈的帮助下，我们表达了考虑边缘智能物联网中恶意软件传播的物联网系统节点和边缘设备之间的网络冲突过程。在这里，物联网系统节点和边缘设备选择自己的策略，并获得由当前状态和策略确定的相应奖励。之后，博弈根据概率分布和参与者的策略随机进入下一阶段，直至达到固定的纳什均衡点。经过理论分析，我们设计并实现了 SGD3QN（随机游戏和决斗双深度 Q 网络）——一种新颖的算法，可在实践中获得减轻物联网恶意软件传播的最佳策略。这里，Douling Double Deep Q-networks充当端到端的决策控制系统，其中物联网恶意软件传播环境作为输入，获取失败或成功的经验来更新网络参数，然后使最优决策输出。然后，我们进行实验模拟，探讨批量大小和重放内存大小对最佳物联网恶意软件传播防御策略选择的影响，并证明所提出的 SGD3QN 辅助决策算法的优势。