## 2 Background and Related Work
### U-Net  
The U-Net model was initially introduced by [PERSON], [PERSON], and [PERSON] in their 2015 paper on biomedical image segmentation [34]. Renowned for its unique symmetry and the use of skip connections, this model has achieved remarkable success across various fields, particularly in medical image processing. The core design principle of U-Net involves an intricate upsampling process and the incorporation of high-resolution features from previous layers through skip connections, ensuring that information is preserved in the deeper layers of the network, which is crucial for precise segmentation of detail-rich areas within images. The U-Net architecture features a symmetric \"U\" shape, divided into two parts: the contraction path and the expansion path. The contraction path, also known as the encoder, progressively reduces the spatial dimensions of the input image while increasing the number of feature channels. Thisprocess involves convolutional layers and max pooling steps, aimed at capturing abstract features of the image and reducing computational complexity. Conversely, the expansion path, or decoder, gradually restores the spatial dimensions through transposed convolution (upsampling) while decreasing the number of feature channels. Each upsampling step is accompanied by skip connections from corresponding downsampling steps, allowing the network to combine high-resolution features with upscaled features, thereby effectively restoring details and local structures of the image.  
A key advantage of the U-Net model is its ability to achieve highly precise results with limited data. This is particularly important for medical image segmentation tasks [35], where high-quality annotated data are often scarce. U-Net addresses data scarcity by effectively utilizing data augmentation techniques such as rotation, scaling, and elastic deformations, significantly enhancing the model's generalization capabilities. Additionally, U-Net excels in handling multi-task learning and multimodal inputs; for example, in processing MRI and CT images, U-Net can integrate information from diverse sources and output precise segmentation through a unified framework. This capability means that U-Net is not limited to medical image processing; for instance, it has found applications in satellite imagery, road detection, and plant disease detection in agriculture. Although initially designed for image segmentation, U-Net's powerful feature extraction and information restoration capabilities make it suitable for a broader range of applications, including detecting cyberattack behaviors. In such tasks, U-Net can assist models in learning and identifying complex patterns in network traffic data or log files, which are often related to anomalous behaviors. By transforming dispersed unstructured data from network logs into meaningful feature representations, U-Net plays a crucial role in the early identification of potential network threats. In the field of network security, the application of U-Net introduces a novel approach, namely, leveraging its validated structure in the domain of imagery to parse and understand network behavior data akin to \"images\". For instance, time series data of network traffic can be treated as \"images\" and processed by U-Net to identify and classify various types of network attacks, such as DDoS and SQL injection. This approach can enhance detection precision while maintaining high computational efficiency and low false positive rates when handling complex data.