## 3 Materials and Method
### Proposed Method  
#### 3.3.1 Overview  
In this study, a novel method for detecting network attack behaviors based on LLMs and synchronized attention mechanisms is proposed. The aim is to enhance the accuracy and efficiency of detecting network attack behaviors by delving into the deep semantic information and contextual relationships within text data. The method is grounded on the utilization of LLMs and synchronized attention mechanisms, as illustrated in Figure 4.  
In the presented methodological framework, the data flow initiates with data input, progresses through various processing levels, and culminates in the output. Initially, data are input into the layers representing text and images, processed via the synchronized attention mechanism, and subsequently refined and integrated through a denoising U-Net to yield the final output. Large Language Models (LLMs) are tasked with processing and understanding textual data. This segment transforms textual data into a format that can be input to neural networks. The core process of the presented method comprises the following steps:  
1. Data preprocessing and vectorization: Collected multisource data undergo preprocessing, including data cleaning, deduplication, tokenization, etc., followed by transformation of text into vector form through advanced word embedding techniques, providing suitable input data for subsequent model training. 2. Application of large language models: Pretrained large language models are employed for deep feature extraction from vectorized data. These models comprehend the profound meanings and complex contextual relations in the text, offering robust technical support for detecting network attack behaviors. 3. Synchronized attention mechanism: The synchronized attention mechanism proposed in this paper enhances the model's focus on the characteristics of network attack behaviors by synchronously processing information within data blocks (intra-block) and across data blocks (inter-block). This mechanism allows the model to capture key information while considering the interactions and dependencies among the information, thereby improving the accuracy of detection. 4. Synchronized loss function: To further optimize the model's training process and enhance detection performance, a synchronized loss function is designed. This function synchronously considers the model's performance across different tasks to optimize and update the model parameters. In Figure 4, \"Diffusion\" denotes a diffusion model, a generative model primarily utilized during the data generation process to restore the details and structure of signals by progressively reducing noise. Within the methodological framework of this study, diffusion models are incorporated to enhance the performance of detecting network attack behaviors, especially when addressing highly nonlinear and complex data features. The roles played by diffusion models in this framework are as follows. Data Enhancement and Recovery:  
Figure 4: This figure displays an overall schematic of the model framework proposed in this paper, including the input processing of multimodal data, the application of the synchronized attention mechanism, and the integration of the denoising U-Net architecture. Initially, the raw data are enhanced (denoted as \(\tau_{B}\) in the figure), then subjected to regularization and noise addition processes (represented by \(\bar{x}\) and \(\epsilon\) in the figure), forming the model’s input. The noised data \(z\) undergo processing through the diffusion process to obtain \(zT\) (where \(T\) represents the time steps in the diffusion process); subsequently, data \(zT\) are processed through the U-Net network for \(T\) steps of denoising to yield \(z^{\prime}\); finally, the regularized data \(\bar{x}\) and \(z^{\prime}\) are fed into the transformer for classification to produce the final result, which is the model’s output.