## 3 Materials and Method
### Experimental Design
#### 3.4.4 Baseline Models  
In this study, several large models widely recognized in the field of artificial intelligence were selected as baseline models for comparison in order to comprehensively verify the effectiveness and superiority of the proposed model for detecting cyberattack behaviors. These baseline models, covering different architectures and learning mechanisms, have demonstrated their powerful performance on multiple NLP tasks. Comparison with these models allows for a more objective assessment of our model's performance in detecting cyberattack behaviors.  
First, the OPT-175B model proposed by OpenAI is discussed [44]. OPT-175B, a large language model based on the transformer architecture, boasts 175 billion parameters, representing the cutting edge of current deep learning technology. Through pretraining on a vast scale of text data, OPT-175B has learned rich language representation capabilities, effectively completing a variety of NLP tasks such as text generation, understanding, and translation. The core of the OPT-175B model is the transformer architecture, which utilizes the self-attention mechanism to capture long-distance dependencies within input sequences, significantly enhancing the model's ability to process sequential data. Next, the LLaMa model [45] proposed by Facebook is mentioned. LLaMa, also a transformer-based large language model, has shown excellent multi-task learning capabilities through pretraining across various languages and tasks. LLaMa particularly emphasizes fine-tuning performance on limited data, achieving rapid adaptation and performance improvement on multiple downstream tasks. When discussing transformer-based models [46], the transformer model itself, introduced by [PERSON] et al. in 2017, must be acknowledged. It was the first to introduce the self-attention mechanism, revolutionizing the approach to sequence modeling and laying the foundation for subsequent developments in deep learning models. The success of the transformer model lies in its ability to process all elements in a sequence in parallel, significantly improving the model's learning efficiency and performance. The BERT model [25], another successful application of the transformer architecture, was introduced by Google in 2018. Through the Masked Language Model and Next Sentence Prediction pretraining tasks, BERT has learned powerful language representation capabilities. The advent of BERT initiated the pretraining revolution in the NLP field, greatly advancing natural language processing technology. Lastly, ChatGLM3-6B [47] is a large model specially designed for chat and dialogue systems. It has learned the ability to understand and generate natural language dialogue through pretraining on a large corpus of dialogue data. ChatGLM3-6B has shown outstanding performance in handling dialogue and interactive tasks, providing strong technical support for building intelligent dialogue systems.  
By comparing our model with these baseline models, not only can the absolute performance of our model in detecting cyberattack behaviors be assessed, its the relative performance across multiple dimensions can be understood as well, revealing the model's strengths and limitations in handling specific types of data and tasks. This comparison can help to validate the effectiveness of our model while providing important references for future model improvements. In conducting the model comparison, attention is paid to the overall performance of the models on the dataset as well as to their performance differences in handling various types of cyberattack behaviors. For example, certain models may excel in detecting malicious software distribution behaviors but perform less well in identifyingphishing websites. By analyzing these differences in depth, a better understanding of each model's characteristics can be achieved, allowing for the targeted selection or design of models to address specific security threats.