Overview of detection techniques for malicious social bots
The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2017-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017275/ |
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| _version_ | 1841539469848608768 |
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| author | Rong LIU Bo CHEN Ling YU Ya-shang LIU Si-yuan CHEN |
| author_facet | Rong LIU Bo CHEN Ling YU Ya-shang LIU Si-yuan CHEN |
| author_sort | Rong LIU |
| collection | DOAJ |
| description | The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed. |
| format | Article |
| id | doaj-art-f2701f6667594ecea002fa5dd7f1e683 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2017-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-f2701f6667594ecea002fa5dd7f1e6832025-01-14T07:14:01ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2017-11-013819721059715701Overview of detection techniques for malicious social botsRong LIUBo CHENLing YUYa-shang LIUSi-yuan CHENThe attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017275/social botsonline social networkfeature engineeringmachine learninggraphcrowdsourcingparallelism |
| spellingShingle | Rong LIU Bo CHEN Ling YU Ya-shang LIU Si-yuan CHEN Overview of detection techniques for malicious social bots Tongxin xuebao social bots online social network feature engineering machine learning graph crowdsourcing parallelism |
| title | Overview of detection techniques for malicious social bots |
| title_full | Overview of detection techniques for malicious social bots |
| title_fullStr | Overview of detection techniques for malicious social bots |
| title_full_unstemmed | Overview of detection techniques for malicious social bots |
| title_short | Overview of detection techniques for malicious social bots |
| title_sort | overview of detection techniques for malicious social bots |
| topic | social bots online social network feature engineering machine learning graph crowdsourcing parallelism |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017275/ |
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