## 5 Conclusions  
We addressed the problem of node data security in a mobile multimedia network, and used an immune method to solve the problem of abnormal detection when nodes are attacked. Game theory was used to optimally allocate mobile user resources. Artificial immune theory uses random coding to generate the initial detector. Non-specific immunity is obtained by negative selection. The risk theory of artificial immunity provides for crossover, variation, and vaccine immune responses. This theory allows for specific immunity. An immune algorithm is used to detect abnormal nodes and to then establish an abnormal detection system for nodes. Obviously, a better theoretical basis is presented. Simulation results showed that the immune algorithm can effectively solve the problem of abnormal detection in node security. Regarding diagnosis, the performance of the algorithm depends on the node's recognition of abnormal data. A certain consumption threshold is set in both time and energy in the diagnosis of the node itself and spatially related neighboring nodes. Through the dual mechanism of self-diagnosis and neighbor node diagnosis, as well as the middle node's diagnosis, the transient abnormal action of nodes are excluded. The accuracy of diagnosis can be effectively improved and the workload is somewhat reduced in the stage of immune computing. The ability to restore a system to a known good state after it has been damaged is more important than making it immune to all attacks. Resilient systems can recover quickly and confidently. As a task-based network, it not only requires data transmission but data fusion and task cooperative control. Node security ensures the confidentiality of task execution, the reliability of data generation, and the security of data transmission. Therefore, detection and recovery of abnormal nodes after diagnosis are important research topics. Due to the fact that a mobile multimedia transmission network has a large number of nodes, to repair all failed nodes in time is bound to lead to system communication difficulties. Game theory studies the strategy and decision-making problems of two or more participants, and can provide novel ideas for the study of network security. A mobile wireless network is characterized by self-organization, the lack of a control center, and dynamic topology. These characteristics determine that each node makes its own decision when communicating. When making a decision, a node may act selfishly and seek a decision beneficial only to itself, or even act maliciously and choose to degrade network performance. Through the game, an abnormal detection system can determine when to start detection, and the priority of node repair can be determined to ensure reliable system operation. In this paper, the status of an abnormal node in the network is determined by calculating its revenue and payment. The game method is based on a Nash equilibrium, and the idea of a dynamic evolutionary game is added. This is because the nodes in the network as participants, have limited cognitive levels, and a limited ability to collect and process information and to reason. The decision-making behavior of participants will be affected by the group environment, and they can only make strategic choices through learning and imitation. Because of bounded rationality, participants in the evolutionary game will not immediately obtain the optimal strategy, and must perform self-adaptive adjustments under the influence of the environment, through continuous learning and trial and error, to find the optimal strategy. Hence, the equilibrium is not the result of a choice, and is only reached by dynamic adjustment and adaptation, and even if the equilibrium is reached, deviations may occur under the premise of environmental changes.