## 2 Related Work  
At present, it is widely recognized in the industry that utilizing entity recognition technology to convert unstructured data into structured data and assist in building knowledge graphs is a direction with prospects and practical significance. However, currently, most named entity recognition is applied in general domains, and there are fewer named entity recognition technologies specifically designed for the field of network security. In recent years, with continuous iterations of software and hardware, deep neural network-based models for named entity recognition have replaced traditional methods based on vocabulary and rules. They have become mainstream approaches in academia and industry with good performance. With the increase in computing power, neural networks have returned to people's attention. In 2003, [PERSON]. [2] first applied Long Short-Term Memory (LSTM) [3] to named entity recognition tasks. Although the performance in entity recognition was not satisfactory, it provided a new research direction. In 2015, [PERSON] et al. [4] designed a neural network model using Bidirectional LSTM (BiLSTM) and Conditional Random Field (CRF) for entity recognition tasks. It achieved excellent results in various English NER tasks and became the state-of-the-art (SOTA) model at that time, as well as the baseline model for many English NER models. [PERSON] et al. [5] proposed a unified neural network architecture and learning algorithm for handling various natural language processing tasks. They trained a word embedding using a language model, and then applied the word embedding to tasks such as part-of-speech tagging, chunking, named entity recognition, etc. By combining word embeddings, convolutional neural networks (CNN), and conditional random fields (CRF), they achieved better results than previous research. However, CNN are unable to handle long text sequences. To enable the model to process long text data, [PERSON] et al. [6] replaced CNN with bidirectional long short-term memory networks (LSTM) as feature extractors to alleviate the problem of long-distance dependencies. However, these methods only consider word-level semantic features and ignore character-level implicit information. [PERSON] et al. [7] proposed a language model that utilizes subword information through character-level CNN and extracts contextual features using LSTM while normalizing them with the SoftMax function. [PERSON] et al. [8], on the other hand, input character sequences into BiLSTM to extract character-level contextual features. They output label probabilities for each character and use Viterbi decoder to convert these probabilities into word-level entity labels. Additionally, this approach also performs well in handling NER tasks in multiple languages. In the English NER field, named entity boundaries align with word boundaries. However, Chinese does not have clear natural boundaries; therefore, Chinese NER usually involves segmenting input text data before feeding it into models for entity recognition. This segmentation operation can introduce errors that lead to inaccuracies in entity recognition. [PERSON] et al. [9] proposed a Chinese radical-level LSTM to capture the pictographic root features, combined with a character-based BiLSTM-CRF model, achieving better performance on Chinese NER tasks. [PERSON] et al. [10] used a lattice structure to integrate lexical information and designed a named entity recognition model called Lattice-LSTM based on the LSTM model. In this model, memory units calculate the weighted sum of the character-level input and all potential words, integrating both lexical and character information. Compared to direct-word segmentation methods, it alleviates errors caused by word segmentation operations. In 2018, Google released the natural language processing model BERT [11], introducing the concept of pre-training models into the field of natural language processing.