## 2 Background and Related Work
### Large Language Models
Specifically, large language models have shown unique advantages in the network attack behavior detection application. For instance, in the analysis of malicious software codes, large language models can conduct an in-depth semantic analysis of the code, effectively identifying hidden malicious behavior patterns [28]. Traditional malware detection methods often rely on specific feature codes or behavior patterns, which may fail when malware authors modify the code through obfuscation, encryption, or variation techniques. In contrast, large language models, by learning the semantic differences between malicious and normal codes, can identify even highly mutated malware. For example, by comparing the structure and semantic features of malicious and normal codes, models can effectively distinguish and label potential malicious codes, even those disguised through complex transformation and obfuscation techniques. Similarly, in the analysis of network traffic and logs [29], large language models demonstrate strong application potential. Attacks such as DDoS and phishing attacks [30] often leave abnormal behavior patterns in network traffic or server logs. By conducting deep learning and semantic analysis on these log data, large language models can quickly identify key information related to attack behaviors from massive data, such as abnormal access frequencies and suspicious request parameters. This deep learning-based approach is more flexible and effective than traditional rule-based detection methods, adapting to the rapid changes and evolution of network attack behaviors. Additionally, for specific types of network attacks such as SQL injection [31] and cross-site scripting (XSS) attacks [32], large language models can provide technical support for precise detection and defense by learning the characteristics and patterns of these attacks. For example, in detecting SQL injection attacks, models can identify illegal SQL operations by analyzing the SQL statements in query logs, effectively blocking malicious database access requests. This method improves detection accuracy while significantly reducing false positives. These applications demonstrate the potential and value of large language models in detecting network attack behaviors, offering more fine-grained in-depth analysis than traditional methods. However, applying large language models to network attack behavior detection faces challenges. On one hand, data in the cybersecurity field typically exhibit high specificity, requiring models to adapt to specific application scenarios; on the other, the constantly changing patterns of network attacks require that models be able to adapt to new attack methods in a timely fashion. A key step in applying large language models to network attack behavior detection is designing appropriate input representations and pretraining tasks that meet specific detection needs. Different detection scenarios necessitate different forms of input representation. For example, in analyzing malicious codes, the input might be a token sequence of the source code, while for log file analysis the input could be a text sequence of log records. In order to adapt these inputs for model processing, they first need to be converted into vector forms understandable by the model, typically achieved through word embedding techniques. The process of word embedding can be mathematically expressed as folows:  
\[\textbf{E}=\text{Embedding}(\textbf{X}). \tag{2}\]Here, **X** represents the input sequence and **E** denotes the vector representation after word embedding. Designing appropriate pretraining tasks is crucial in order for large language models to learn valuable knowledge for attack behavior detection.