## 3 Materials and Method
### Experimental Design  
#### 3.4.1 Hardware and Software Platform  
In this study, constructing a high-performance experimental environment was deemed crucial, as the training and testing of models for detecting cyberattack behaviors demands extensive computational resources and data processing capabilities. Consequently, a platform combining hardware and software was established to provide robust support tailored to the requirements of developing deep learning models. The hardware platform was based on a high-performance computing system, at the core of which lay multiple Intel Xeon processors (Intel, Beijing, China). These processors, known for their high core count, are capable of parallel processing of large datasets, offering strong support for complex computational tasks. Additionally, NVIDIA Tesla V100 (Nvidia, Beijing, China) graphic processing units (GPUs) were equipped. Renowned in the deep learning domain for their exceptional computational power, these GPUs significantly accelerate the training process, especially for deep neural network models characterized by complex structures and numerous parameters. The memory was configured to 256 GB RAM, allowing the system to efficiently handle large datasets without encountering data swapping issues due to insufficient memory, ensuring smooth data processing and model training. For the storage system, SSD drives were chosen as the data storage solution, as their rapid data read and write capabilities ensure efficient data processing, particularly during extensive input/output operations, significantly reducing waiting times.  
On the software side, the experimental environment was installed with the Python 3.8 programming language. Python, favored in deep learning research for its concise syntax, powerful library support, and extensive community resources, became the language of choice. To support the development and training of complex models, TensorFlow 2.1.5 and PyTorch 1.8, two deep learning frameworks, were deployed in the environment. Developed by Google, TensorFlow is widely acclaimed in both industry and academia for its flexible architecture and robust ecosystem. PyTorch, favored for its intuitive API and dynamic computation graph feature, is particularly suitable for rapid prototyping and experimentation. These frameworks offer a wealth of tools for model construction and training; in addition, they support GPU acceleration, making full use of the computational resources of our hardware platform. For text data processing, two natural language processing libraries, NLTK (Natural Language Toolkit) and Spacy, were selected. NLTK, a powerful natural language processing toolkit for Python, provides a wide range of text processing libraries and data resources suitable for linguistic research and teaching. Spacy is widely used in the industrial and research sectors for its efficient text processing capabilities, especially excelling in text analysis and entity recognition. By integrating these software libraries and tools, efficient completion of tasks such as text data preprocessing and feature extraction was facilitated, providing a solid data foundation for model training. In summary, through meticulous hardware configuration and software selection, the experimental environment of this study offers robust support for deep learning research in detecting cyberattack behaviors, providing an efficient and stable platform for the current research task while reserving ample space and possibilities for future research exploration. Through such experimental design, it is anticipated that research progress in the field of cybersecurity will be propelled, contributing to the construction of a more secure cyber-environment.