In wireless Internet of Things (IoT) systems, the multi-input multi-output (MIMO) and cognitive radio (CR) techniques are usually involved into the mobile edge computing (MEC) structure to improve the spectrum efficiency and transmission reliability. However, such a CR based MIMO IoT system will suffer from a variety of smart attacks from wireless environments, even the MEC servers in IoT systems are not secure enough and vulnerable to these attacks. In this paper, we investigate a secure communication problem in a cognitive MIMO IoT system comprising of a primary user (PU), a secondary user (SU), a smart attacker and several MEC servers. The target of our system design is to optimize utility of the SU, including its efficiency and security. The SU will choose an idle MEC server that is not occupied by the PU in the CR scenario, and allocates a proper offloading rate of its computation tasks to the server, by unloading such tasks with proper transmit power. In such a CR IoT system, the attacker will select one type of smart attacks. Then two deep reinforcement learning based resource allocation strategies are proposed to find an optimal policy of maximal utility without channel state information(CSI), one of which is the Dyna architecture and Prioritized sweeping based Edge Server Selection (DPESS) strategy, and the other is the Deep Q-network based Edge Server Selection (DESS) strategy. Specifically, the convergence speed of the DESS scheme is significantly improved due to the trained convolutional neural network (CNN) by utilizing the experience replay technique and stochastic gradient descent (SGD). In addition, the Nash equilibrium and existence conditions of the proposed two schemes are theoretically deduced for the modeled MEC game against smart attacks. Compared with the traditional Q-learning algorithm, the average utility and secrecy capacity of the SU can be improved by the proposed DPESS and DESS schemes. Numerical simulations are also presented to verify the better performance of our proposals in terms of efficiency and security, including the higher convergence speed of the DESS strategy.

在无线物联网（IoT）系统中，多输入多输出（MIMO）和认知无线电（CR）技术通常包含在移动边缘计算（MEC）结构中，以提高频谱效率和传输可靠性。但是，这样的基于CR的MIMO IoT系统将遭受来自无线环境的各种智能攻击，即使IoT系统中的MEC服务器也不够安全且容易受到这些攻击。在本文中，我们研究了认知MIMO IoT系统中的安全通信问题，该系统包括一个主要用户（PU），一个次要用户（SU），一个智能攻击者和几个MEC服务器。我们系统设计的目标是优化SU的效用，包括其效率和安全性。在CR场景中，SU将选择PU并不占用的空闲MEC服务器，并通过以适当的发射功率卸载此类任务来为服务器分配适当的计算任务卸载速率。在这种CR IoT系统中，攻击者将选择一种类型的智能攻击。然后，提出了两种基于深度强化学习的资源分配策略，以找到没有信道状态信息（CSI）的最大效用的最优策略，其中一种是Dyna架构和基于优先级清扫的边缘服务器选择（DPESS）策略，另一种是基于深度Q网络的边缘服务器选择（DESS）策略。具体而言，由于使用了经验回放技术和随机梯度下降（SGD），经过训练的卷积神经网络（CNN）大大提高了DESS方案的收敛速度。此外，从理论上推导了两种针对智能攻击的MEC博弈的纳什均衡和存在条件。与传统的Q学习算法相比，提出的DPESS和DESS方案可以提高SU的平均效用和保密能力。还提供了数值模拟，以验证我们的建议在效率和安全性方面的更好性能，包括DESS策略的更高收敛速度。