Cerberus：基于深度学习的跨网站社交机器人检测系统

汤家伟; 刘育杉; 高敏; 宫庆媛; 王新; 陈阳

doi:10.11959/j.issn.2096-6652.202436

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Cerberus：基于深度学习的跨网站社交机器人检测系统

专题：社会计算与社会智能 | 更新时间：2025-01-24

- Cerberus：基于深度学习的跨网站社交机器人检测系统
- Cerberus: cross-site social bot detection system based on deep learning
- 智能科学与技术学报 2024年6卷第4期页码：482-494
- 作者机构：
  
  1.复旦大学计算机科学技术学院，上海 200438
  2.复旦大学智能复杂体系基础理论与关键技术实验室，上海 200438
- 作者简介：
  
  [ "汤家伟（1997- ），男，复旦大学计算机科学技术学院科研助理，主要研究方向为社交网络、机器学习、用户行为大数据分析。" ]
  [ "刘育杉（1999- ），女，复旦大学计算机科学技术学院硕士生，主要研究方向为在线社交网络用户数据挖掘。" ]
  [ "高敏（1997- ），女，复旦大学计算机科学技术学院博士生，主要研究方向为社交网络用户行为建模、图深度学习、网络科学等。" ]
  [ "宫庆媛（1991- ），女，博士，复旦大学智能复杂体系基础理论与关键技术实验室青年副研究员，主要研究方向为在线社交网络用户行为大数据。" ]
  [ "王新（1973- ），男，博士，复旦大学计算机科学技术学院党委书记、教授、博士生导师，主要研究方向为新一代互联网体系结构、无线与移动网络、数据中心网络、社交网络、网络编码的应用。" ]
  [ "陈阳（1981- ），男，博士，复旦大学计算机科学技术学院教授、博士生导师，上海市智能信息处理重点实验室副主任，主要研究方向为社会计算、计算机网络、大规模用户行为数据挖掘等。" ]
- 基金信息：
  
  国家重点研发计划(2022YFB3102901);国家自然科学基金项目(62072115;62102094);上海市科技创新行动计划(22510713600)
- DOI：10.11959/j.issn.2096-6652.202436
  中图分类号： TP39
- 收稿日期：2024-08-07，
  
  修回日期：2024-09-29，
  
  纸质出版日期：2024-12-15
- 稿件说明：
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汤家伟,刘育杉,高敏等.Cerberus：基于深度学习的跨网站社交机器人检测系统[J].智能科学与技术学报,2024,06(04):482-494.

TANG Jiawei,LIU Yushan,GAO Min,et al.Cerberus: cross-site social bot detection system based on deep learning[J].Chinese Journal of Intelligent Science and Technology,2024,06(04):482-494.
汤家伟,刘育杉,高敏等.Cerberus：基于深度学习的跨网站社交机器人检测系统[J].智能科学与技术学报,2024,06(04):482-494. DOI： 10.11959/j.issn.2096-6652.202436.

TANG Jiawei,LIU Yushan,GAO Min,et al.Cerberus: cross-site social bot detection system based on deep learning[J].Chinese Journal of Intelligent Science and Technology,2024,06(04):482-494. DOI： 10.11959/j.issn.2096-6652.202436.

摘要

社交网站吸引了数十亿用户，影响着人们的生活方式。社交网站作为开放平台，注册加入的门槛较低，社交机器人能够轻易地注册，并进行舆论导向控制、不实信息传播等有害活动，以谋取利益。单一社交网站的机器人检测系统往往需要依赖用户的历史行为数据进行分析。因此，社交机器人在被识别出之前往往已经成功实施了恶意攻击。为尽早地识别出社交机器人，提出了跨网站社交机器人检测系统Cerberus。Cerberus可以解决用户早期在单个社交网站上数据不充足导致的用户识别“冷启动”的问题。Cerberus使用用户在Medium网站上的个人信息和历史活动信息，对用户链接在Twitter上的账号是否为社交机器人账号进行预测。结果表明，该系统的AUC值可达0.7552，具有良好的识别准确性。

Abstract

Social networking sites have attracted billions of users and influence people's lifestyles. However

as open platform with low requirements for registration and joining

it is inevitable that social bots are able to easily register and do harmful things such as controlling public opinions and spreading inaccurate information for profit. Nevertheless

single-site social bot detection systems often rely on historical behavioral data to identify bots

and the detection occurred after the social bots have implemented their attacks. To identify social bots as early as possible

this paper proposed Cerberus

a cross-site social bot detection system. Cerberus can solve the cold-start problem of user identification caused by insufficient user data on a single platform at an early stage. Cerberus used personal information and historical activity on the Medium website of users to make prediction about whether a user's account on Twitter was a social bot. The results from our experiments show that the AUC score of Cerberus can reach 0.7522

which has good recognition accuracy.

关键词

Keywords

references

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