# A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks
###### Abstract  
With the development of IoT technology, central cloud servers and edge-computing servers together form a cloud-edge communication network to meet the increasing demand for computing tasks. The data transmitted in this network is of high value, so the ability to quickly and accurately predict the traffic load of each link becomes critical to ensuring the security and stable operation of the network. In order to effectively counter the potential threat of flood attacks on network stability, we combine the Bi-directional Gated Recurrent Unit (BiGRU) model with the Dung Beetle Optimizer (DBO) algorithm to design a DBO-BiGRU short-term traffic load prediction model. Experimental validation on a public dataset shows that the proposed model has better prediction accuracy and fit than the mainstream models of RNN, LSTM, and TCN.  
edge computing; IPSec; data security; short-term time-series prediction 202420155202420242024202420242024202420242024202024202420242024202420202420242020242020242020242024202024202024202024202420242020242024202420202420242020242024202420242024202420242024202420242024202420242024202420 and reliability. The edge servers effectively reduce the computational and storage load on the central cloud servers, thereby enhancing the system's ability to handle the burgeoning demands of IoT applications in a more robust and sustainable manner [17; 18; 19; 20; 21].  
In practical application scenarios, edge servers are capable of communicating not only with one another but also with centralised cloud servers. This bidirectional data exchange capability establishes a highly dynamic and interconnected cloud-edge communication network, as illustrated in Figure 2. In terms of edge computing, this network plays a key role in the efficient and decentralised processing of data generated by a large number of IoT devices. However, the information traversing this network often has significant business value, ranging from sensitive user data to critical operational insights. Therefore, it is important to ensure the security of data transmissions within this network.  
In light of the considerable commercial and strategic value of the data traversing cloud-edge networks, there is a pressing and imperative need for comprehensive data protection measures. This paper addresses this critical issue by exploring secure interconnection mechanisms for servers operating in edge-computing environments. In particular, we concentrate on the susceptibility of these systems to flooding attacks, a category of network-based threats that can overwhelm servers with malicious traffic, resulting in service disruption or data breaches. To reduce this risk, current researchers focus on optimising traffic load prediction. Although the current research results show excellent performance in network traffic prediction, on the one hand, the training and fitting time of the model is long, and on the other hand, a series of manual tuning processes with the help of experts' experience must be carried out before achieving better results, which involves subjective factors and easily falls into the dilemma of local optimality.  
In order to meet the security and stability requirements of data transmission between servers in edge-computing scenarios, we propose a new approach based on the fast and accurate prediction of traffic patterns for each time period, which combines the BiGRU model with the DBO algorithm. This approach allows for early detection and response to potential flooding attacks, thereby improving the security and resilience of cloud-edge communication networks.  
Figure 1: Application diagram of edge-computing paradigm.  
The structure of this paper is organized as follows: Section 2 provides a brief overview of related literature. Section 3 introduces short-term traffic load prediction based on DBO-BiGRU. Section 4 presents simulation experiments, data comparison, and results analysis. Finally, the main improvements are summarized in Section 5.