## 2 Background and Related Work
### Related Work  
#### 2.2.1 Detection of Illegal Websites  
Identification methods for illegal websites, such as gambling, pornography, phishing websites, etc., can be broadly categorized into two groups. The first category involves the utilization of supervised learning techniques, while the second category employs unsupervised methods. 1. Supervised learning for illegal website detection  
Supervised methods for illegal website detection rely on labeled datasets to train models and learn the distinguishing features of illegal websites. This approach offers the advantage of high accuracy; however, it necessitates a substantial amount of labeled data. Here are a few commonly employed supervised approaches:  
**Method based on machine learning. ** The method based on machine learning is a commonly used detection method for illegal websites, which uses a variety of machine learning algorithms to train the model. The method first extracts various features from the website, such as the title of the page, keywords, description, HTML tags, etc. Then, it uses these features to train a classifier to classify illegal websites and normal websites. Commonly used machine learning algorithms include Naive Bayes [10], Support Vector Machines [11], decision trees [12], random forests [13], etc. The Naive Bayesian algorithm is a classic machine learning algorithm, which is based on Bayesian theorem and feature independence assumptions for classification. A Support Vector Machine is a classifier that performs classification based on the margin maximization principle. A decision tree is a tree-based classifier that processes data hierarchically to achieve higher accuracy. A random forest is an ensemble learning algorithm that performs classification by combining multiple decision trees. [PERSON] et al. [14] used seven different classification algorithms and features based on natural language processing (NLP) to detect phishing websites. They used the URL features of phishing websites to achieve the best performance on the random forest algorithm. [PERSON] et al. [15] proposed MLSELM, which combines MLP (Multilayer Perceptron), KNN (K-Nearest Neighbors), RF (random forest) and other algorithms to form a three-layer classifier for four phishing datasets. Evaluations were performed and the final results outperformed the baseline models on the dataset. **Method based on deep learning. ** The deep learning-based method uses a multi-layer neural network for classification. This method first vectorizes the attribute information of the website, and then uses the deep neural network to classify the data. [PERSON] et al. [16] proposed a phishing website detection framework based on deep learning, which combines various strategies such as white list filtering, black list interception and model prediction to improve detection accuracy. Finally, the RNN-GRU model achieved the best performance on multiple datasets. [PERSON] et al. [17] considered the semantic information of different scales, and passed the text information such as URL, Title, and Body Text into the CNN model and the LSTM model for learning, respectively. Feature fusion was performed at the output layer, and, finally, the judgment result was output. In reality, the application model successfully detected 3016 phishing websites. [PERSON] et al. [18] proposed a robust end-to-end framework, Porn2 Vec, that uses comparative learning to detect pornographic websites. They model pornographic websites through heterogeneous graphs composed of websites, webpages, images, texts, and their interactions. The pornographic website detection task is transformed into a heterogeneous graph node classification task, and the robustness of the model is improved through the representation of multiple data. [PERSON] et al. [19] fused visual and text semantic features through the self-attention mechanism, and fused the prediction results of the image classifier, text classifier and multi-modal classifier in the later stage, which achieved excellent results on their own collected gambling datasets.