Fraud web page detection based on bidirectional LSTM and attention mechanism

Le résumé fourni par la source

With the increasing popularity of the Internet, network security issues are also increasing and there are more and more fraudulent web pages, which also brings great obstacles to the governance of network security. However, for the detection of fraudulent web pages, most of the previous detection methods are based on web page characteristics. Multiple information on web page content, such as web page text, HTML, images, and other content is also an important feature for detecting and discovering fraudulent web pages. In this paper, the text dataset of fraudulent web pages obtained by the crawler technique is used to extract the features of the text content sentences using the bidirectional LSTM model, and the feature weights of the text dataset of fraudulent web pages are enhanced by introducing an attention mechanism to obtain a feature vector representation of the text data sentences and then the classification detection is carried out. The experiments show that the accuracy and F1 values of the bidirectional LSTM model are higher than those of other models. The bidirectional LSTM model on the addition of the attention mechanism performed better, yielding an accuracy result of 90.85%, which is a 2.7% increase in accuracy and 3.09% increase in F1 value compared to the bidirectional LSTM model. Therefore, the method is effective in detecting fraudulent web pages and has some practicality in cyber security governance.

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