## 1 Introduction
In the field of detection of abnormal nodes research, [PERSON] has proposed a distributed fault detection and recovery algorithm [6], which can effectively detect relay node failure. In this algorithm, any failed relay node is identified by its neighbors according to the neighborhood table associated with them. The algorithm complexity is high. [PERSON] has proposed a fault detection scheme for the identification of faulty sensor nodes [7]. In this scheme, the coordination between the two execution levels is not well structured and the simulation process is too idealized. [PERSON] et al. has suggested a wireless sensor network hardware fault detection method using a simple Bayes framework for detecting node energy failure [8]. However, data cannot be collected in real time when a fault occurs, nor can the fault feature be extracted in real time. A cluster-based fault detection algorithm has been proposed by [PERSON], in which a node confidence mechanism is introduced [9]. The disadvantage is that only dynamic tolerance is considered, and dynamic detection is not performed. From the perspectives of intelligence and automation, mobile multimedia network nodes have the characteristics of real-time, dynamic, and multi-source data, and node security must consider the dynamic changes of data in real time. In the literature mentioned above, most of the proposed algorithms are static detection algorithms based on data. These algorithms do not consider the difference in abnormal data characteristics caused by data dynamics and multiple sources. The biological immune system has information processing mechanisms, such as memory learning, feedback regulation, and decentralized and distributed autonomous mechanisms. An artificial immune system established according to biological immune theory includes methods such as immune recognition, immunology learning, immune memory, and cloning selection. Both the biological immune system and a distributed self-organizing wireless sensor network maintain system stability in a changing environment. The immune system structure provides a novel solution to the problem of node security in a mobile network. [PERSON] has proposed an immuno-inspired algorithm for node failure [10]. The proposed immune excitation scheme that uses a multimodal mechanism has good reliability, but the operating environment it adapts to is specific, not universal. [PERSON] has proposed collaborative monitoring and intrusion detection mechanisms inspired by the body's immune system [11]. It allows nodes to monitor their neighbors and collaborate to identify intruders. The defect of this algorithm is that it does not consider the correlation and response mechanism between nodes, which leads to a large resource occupation. [PERSON] et al. has proposed an immune intrusion detection mechanism for a wireless sensor network tree structure [12]. Two immunization strategies, namely unified immunization and temporary immunization, are studied in sensor viruses. The algorithm is only effective for a single sensor virus attack and not for most attacks. [PERSON] et al. have introduced an optimized neuro-immune system to predict sensor records [13]. In this method, only the immune theory is used to establish the network model, and no further research was done on the abnormal detection of nodes. Author has done some previous work about abnormal detection with immune theory, including negative selection, clone selection, vaccine and detector's research [14]. The contribution of this literature is the construction of an IDS inspired by biological immune principles and functions, such as resisting viruses and their variations in the biological immune system. The immune idea is incorporated into the design of IDS, and an intrusion detection system that can detect novel attacks adaptively under the premise of lower data requirements is constructed. Thus, the security of the edge computing scenario is improved.