A method and system for detecting navy based on improved dbn model

Abstract

The invention relates to a navy detection method based on an improved DBN model. The method specifically includes the following steps that firstly, training and detection are carried out on an original DBN model through a classified data set, and the improved DBN model is built; secondly, data in a data set to be classified are input into the improved DBN model to be classified, and identification of navy users is completed. 'Fly navies' are identified by combining the navy detection method with a DBN and the PSO, then the DBN model is built, training is carried out on the DBN model through the classified data set, user data to be classified are classified through the acquired model finally, and accordingly 'fly navy' identification is achieved. By means of the method, a BP neural network algorithm is improved, high judgment accuracy can be guaranteed, the training time is short, and the process will not be caught in the locally optimal solution easily.

Description

technical field [0001] The invention relates to a water army detection method and system based on an improved DBN model. Background technique [0002] With the popularity of social networks, forums have become one of the most popular online applications. However, the open nature of online forums determines that it is difficult to strictly supervise the information in the forums, which has led to the emergence of a group of online trolls who deliberately spread certain remarks for the purpose of profit. From the "July 23" bullet train accident sky-high compensation incident to the Qin Huohuo incident, cyber trolls have had a serious impact on the network environment and even social order. It can be seen that the identification and supervision of cyber trolls is imminent. [0003] There are two ways to supervise online trolls: one is to judge each post and delete troll posts; the other is to judge each user and delete posts for troll users. Even pursue its legal responsibili...

AI Technical Summary

Problems solved by technology

The existing "fly army" generally uses algorithms such as decision trees, Bayesian networks, KNN, and neural networks. In the design process of the previous algorithms, it is necessary to set the values ​​of key parameters based on historical experience to reflect user Each aspect of the behavior has a different influence on the judgment result. This method is highly subjective and seriously affects the judgment accuracy of the algorithm; Determining the model parameters can objectively reflect the impact of different aspects of user behavior on the final result, but the training process takes too long, and it is easy to fall into a local optimal solution due to improper initial weight setting of the network

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