Gambling web page recognition algorithm design based on deep residual neural network - Zhang Cong; Zhang Heng; Zhang Likun; Zhao Tong; Deng Guiying (Dianzi Jishu Yingyong)
The Internet has an important impact on people′s life and work. However, there are a large number of harmful gambling websites hidden in cyberspace, which is easy to cause losses and troubles to netizens, it can even disturb society order. Therefore, it is of great significance to study the efficient recognition method of such websites. In this paper, the deep residual neural network is used to solve the problem of gambling web page recognition, and the algorithm GamblingRec is designed based on principle of deep residual network. The results show that the accuracy of GamblingRec reaches 95.16%, and the positive sample recall rate is 93.21%，which indicates that the method based on deep residual neural network can be applied for gambling web page recognition, and can achieve high recognition performance.