基于生成对抗网络的社交机器人检测  

Social Bots Detection Based on Generative Adversarial Networks

作　　者：李阳阳[1] 杨英光 LI Yang-yang;YANG Ying-guang(National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (PSRPC), China Academy of Electronics and Information Technology, Beijing 100041, China;School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China)

机构地区：[1]中国电子科技集团公司电子科学研究院社会安全风险感知与防控大数据应用国家工程实验室,北京100041 [2]中国科学技术大学网络空间安全学院,安徽合肥230026

出　　处：《计算机与现代化》2022年第3期1-6,共6页Computer and Modernization

基　　金：国家自然科学基金资助项目(U20B2053);海南省重大科技计划项目(ZDKJ2019008)。

摘　　要：推特作为一个有着上亿活跃用户的社交媒体,有近15%的机器账户通过自动化程序被控制,其中一些机器账号为传播恶意信息的恶意账号。虽然研究者开发了大量复杂的机器账号检测方法,但这些方法都需要有关机器账号的先验知识,并且泛化性不高。为了解决这些问题,提出使用生成对抗网络中的判别器来进行机器账号检测,使得只需要真实账号的示例即可得到良好的检测模型,并在一个流行数据集做实验,AUC达到了94%的分类效果。Twitter is a social media with hundreds of millions of active users.Nearly 15%of bot accounts are controlled by automated programs.Some of these bot accounts are malicious account that spread malicious information.Although researchers have developed a large number of sophisticated bot account detection methods,they all require prior knowledge of bot accounts which are lack of generalization.In order to solve these problems,this paper proposes to use the discriminator from generative adversarial network for bot account detection.This makes it possible to obtain a good detection model with the examples of real accounts.Experiments on a popular dataset show that the AUC achieves 94%classification effect.

关 键 词：社交机器人 生成对抗网络 机器账号检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]