“Anonymous Market Product Classification Based on Deep Learning”, 2019-12 (; backlinks; similar):
With the rapid development of Internet technology, the abuse of dark networks and anonymous technology has brought great challenges to network supervision. Therefore, it is important to study the anonymous market.
In this paper, we propose a single-mode multivariate classification model for anonymous market product classification. Divide anonymous markets products into 5 categories. Our algorithm uses the word vector embedded in a convolutional neural network based on Word2vec training.
Compared with the simple machine learning classification model, the accuracy of the single-mode multivariate classification model on the test set is 91.84%.
By studying the classification of anonymous market products, law enforcement personnel can better supervise anonymous market of illegal products and maintain network security.