## 4 Results and Discussion  
### Detection Results for Cyberattack Behaviors  
This study conducted experiments on three distinct datasets, namely, server logs, financial behaviors, and comment data, to validate the effectiveness of the proposed method in detecting cyberattack behaviors. The experimental design aimed to demonstrate whether the method combining synchronous attention mechanisms with large language models could achieve higher Precision, Recall, and Accuracy compared to existing advanced models such as Transformer, BERT, OPT-175B, LLaMa, and ChatGLM3-6B, indicate more effective identification and classification of cyberattack behaviors. The experimental results, as shown in Tables 2-4, indicate that the proposed method outperformed the others across all three datasets, achieving Precision, Recall, and Accuracy of 0.93, 0.91, and 0.92 on the server log dataset, 0.90, 0.87, and 0.89 on the financial behavior dataset, and 0.95, 0.93, and 0.94 on the comment dataset, respectively. In contrast, the other models (Transformer, BERT, OPT-175B, LLaMa, and ChatGLM3-6B), despite showcasing high performance, fell short of the proposed method across all evaluated metrics. These results distinctly illustrate the superiority of the proposed method in handling the task of cyberattack behavior detection. The theoretical analysis of these experimental results is based on several points. First, large language models, particularly optimized ones such as BERT, LLaMa, and ChatGLM3-6B, have proven their powerful capabilities in understanding the deep meanings and contextual relations of text. However, by incorporating synchronous attention mechanisms, the proposed method has enhanced ability to capture key information and understand the interactions between different levels of information. This is especially effective in multimodal data processing scenarios, where it can more efficiently integrate information from various data sources, thereby improving the model's comprehensive judgment ability. Second, the design of the synchronous attention mechanism allows the model to consider \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** \\ \hline Transformer & 0.85 & 0.82 & 0.84 \\ BERT & 0.87 & 0.84 & 0.86 \\ OPT-175B & 0.89 & 0.86 & 0.88 \\ LLaMa & 0.91 & 0.88 & 0.90 \\ ChatGLM3-6B & 0.93 & 0.90 & 0.92 \\ Ours & 0.95 & 0.93 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Attack detection results for comment data. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** \\ \hline Transformer & 0.81 & 0.77 & 0.78 \\ BERT & 0.83 & 0.79 & 0.80 \\ OPT-175B & 0.85 & 0.82 & 0.83 \\ LLaMA & 0.88 & 0.85 & 0.86 \\ ChatGLM3-6B & 0.90 & 0.88 & 0.89 \\ Ours & 0.93 & 0.91 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Attack detection results for server logs. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** \\ \hline Transformer & 0.81 & 0.77 & 0.78 \\ BERT & 0.83 & 0.79 & 0.80 \\ OPT-175B & 0.85 & 0.82 & 0.83 \\ LLaMA & 0.88 & 0.85 & 0.86 \\ ChatGLM3-6B & 0.90 & 0.88 & 0.89 \\ Ours & 0.93 & 0.91 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Attack detection results for financial behaviors. supplementary information from other data sources while capturing information from a single data source. This aspect is crucial in detecting cyberattack behaviors, as such behaviors often involve complex patterns and cross-verification of information from multiple sources. Additionally, the proposed method fine-tunes the model's focus on different tasks during the learning process through the design of a synchronous loss function, ensuring a better balance between synchronous processing and feature extraction, leading to significant improvements in Accuracy, Recall, and Precision.