## 3 Materials and Method
### Proposed Method
#### 3.3.2 Large Model for Attack Behavior Detection  
A model integrating synchronous attention mechanisms and a denoising U-Net architecture was developed to enhance the detection capabilities for complex cyberattack behaviors. Designed with multimodal inputs, this model is capable of processing various data types, including text and images, to utilize multisource information for attack detection comprehensively. The core components are as follows:  
1. Input layer: The model accepts preprocessed multimodal data, such as text sequences transformed into vectors via word embedding and image data standardized.
2. Synchronous attention layer: Text data are processed using a self-attention mechanism, while image data are handled through a denoising U-Net architecture. The introduction of a synchronous mechanism is crucial, enabling the model to consider information from other modalities while processing one type of data modality. For instance, the text processing component relies on its sequence information as well as on relevant features from image data: \[\text{SA}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}+S\bigg{)}V\] (13) where \(Q,K,V\) represent the query, key, and value matrices, respectively, \(d_{k}\) denotes the dimension of the key vectors, and \(S\) is the synchronous signal from other modalities.
3. Denoising U-Net architecture: Image data undergo feature extraction and denoising using the U-Net structure, in which skip connections maintain the spatial information of image features. The denoising step removes irrelevant noise, preserving key features of attack behaviors.
4. Fusion and classification layer: Processed text and image features are fused and then classified through a fully connected layer to determine the type of attack behavior.  
The introduction of a synchronous attention mechanism enables the model to fully leverage complementary information from other modalities while processing one type of data, enhancing the detection capabilities for complex attack behaviors. The use of a denoising U-Net architecture capitalizes on its advantages in feature extraction and spatial information retention. Denoising further improves the model's feature quality and generalization capability. This model emphasizes synchronous processing of multimodal data and denoising, addressing aspects not covered by models that focus on a single modality or lack a synchronous processing mechanism. By combining synchronous attention with the denoising U-Net, the model is able to processes complex semantic information within textual data while efficiently utilizing spatial features from non-textual data, significantly enhancing its detection accuracy and robustness. Furthermore, adjusting the weight of the synchronous signal \(S\) allows for more effective integration of information from different modalities, improving recognition precision for attack behavior characteristics.  
In summary, the large model proposed in this study demonstrates significant performance advantages on the task of network attack behavior detection, particularly through its unique design in synchronous processing of multimodal data and efficient denoising of image data. The application of these design concepts and methodologies enhances the model's effectiveness in the field of cybersecurity and provides valuable insights for handling other complex multimodal data analysis tasks.