## 2 Background and Related Work
### Attention Mechanism  
The proposal of the attention mechanism in the deep learning field was inspired by the human visual attention mechanism [36]. The basic idea is that the model can pay more attention to the important parts of the input data while paying less attention to the less important parts during information processing. This mechanism enables the model to dynamically adjust its focus on the input data, thereby improving processing efficiency and enhancing model performance and accuracy. In deep learning models, especially when processing sequence data, the attention mechanism is implemented by calculating the mutual weights between input features to reflect their importance. The initial attention mechanism can be expressed as shown in Equation (1) and Figure 3.  
One of the key challenges in detecting network attack behaviors is accurately and quickly identifying attack activities from vast amounts of network data [37]. These data include, but are not limited to, server logs, network traffic, and user behavior data, which contain both normal business activity information and potential malicious attack information. The attention mechanism, by providing models with the ability to filter and focus on key information, greatly enhances detection accuracy and efficiency. For instance, in analyzing network traffic data models need to identify which traffic patterns are normal and which may belong to DDoS attacks [38]. By incorporating an attention mechanism, models can focus more on key features such as abnormal access frequency and request sources, thereby improving their identification accuracy. Specific attention models can be designed for scenarios involving network attack behavior detection; for example, models employing self-attention mechanisms [39] are particularly suitable for processing sequence data such as log files. In this scenario, \(Q\), \(K\), and \(V\) might represent different representationsof the same log data sequence, allowing the model to learn internal dependencies in the sequence in order to identify potential attack patterns. Furthermore, for complex attack behavior detection tasks, multi-head attention mechanisms can be designed to further enhance model performance:  
\[\text{Multi-Head}(Q,K,V)=\text{Concat}(\text{head}_{1},\text{head}_{2}, \ldots,\text{head}_{n})W^{O}, \tag{5}\]  
\[\text{where head}_{i}=\text{Attention}(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}). \tag{6}\]  
Here, \(W_{i}^{Q},W_{i}^{K},W_{i}^{V}\) are different weight matrices used to project \(Q\), \(K\), \(V\) into different representation spaces, with \(W^{O}\) being the weight matrix of the output layer. Through this method, the model can learn different aspects of the input data in parallel in multiple representation spaces, capturing richer information to improve detection accuracy and the ability to identify complex attack behaviors. The attention mechanism shows strong application potential in detecting specific types of network attacks, such as SQL injection and XSS attacks [40,41]. Through deep learning of the characteristics of attack behaviors, models can accurately identify key operations and patterns in attacks. For example, when detecting SQL injection attacks, models can identify atypical query patterns or structurally abnormal query statements by learning the differences between normal and malicious SQL queries, thereby effectively identifying and blocking SQL injection attacks. Moreover, models can accurately identify attack behaviors in more complex scenarios, such as phishing and malware dissemination, as the attention mechanism dynamically adjusts the model's focus to accurately identify traces of attack behaviors from extensive network communication data [42,43]. For instance, in phishing email detection, models can focus on deceptive vocabulary or sentence structures in email content to effectively identify and intercept phishing emails. The introduction of the attention mechanism provides a highly efficient, flexible, and powerful technical means for detecting network attack behaviors.