Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor
Malicious social media bots are disseminators of malicious information on social networks and seriously affect information security and the network environment. Efficient and reliable classification of social media bots is crucial for detecting information manipulation in social networks. Aiming to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/12/5482 |
| _version_ | 1797530280086470656 |
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| author | Xiujuan Wang Qianqian Zheng Kangfeng Zheng Yi Sui Siwei Cao Yutong Shi |
| author_facet | Xiujuan Wang Qianqian Zheng Kangfeng Zheng Yi Sui Siwei Cao Yutong Shi |
| author_sort | Xiujuan Wang |
| collection | DOAJ |
| description | Malicious social media bots are disseminators of malicious information on social networks and seriously affect information security and the network environment. Efficient and reliable classification of social media bots is crucial for detecting information manipulation in social networks. Aiming to correct the defects of high-cost labeling and unbalanced positive and negative samples in the existing methods of social media bot detection, and to reduce the training of abnormal samples in the model, we propose an anomaly detection framework based on a combination of a Variational AutoEncoder and an anomaly detection algorithm. The purpose is to use Variational AutoEncoder to automatically encode and decode sample features. The normal sample features are more similar to the initial features after decoding; however, there is a difference between the abnormal samples and the initial features. The decoding representation and the original features are combined, and then the anomaly detection method is used for detection. The results show that the area under the curve of the proposed model for identifying social media bots reaches 98% through the experiments on public datasets, which can effectively distinguish bots from common users and further verify the performance of the proposed model. |
| first_indexed | 2024-03-10T10:26:47Z |
| format | Article |
| id | doaj.art-dec7ab8753f14db386124ecd74432a04 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:26:47Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-dec7ab8753f14db386124ecd74432a042023-11-21T23:58:02ZengMDPI AGApplied Sciences2076-34172021-06-011112548210.3390/app11125482Detecting Social Media Bots with Variational AutoEncoder and k-Nearest NeighborXiujuan Wang0Qianqian Zheng1Kangfeng Zheng2Yi Sui3Siwei Cao4Yutong Shi5Information Technology Institute, Beijing University of Technology, Beijing 100124, ChinaInformation Technology Institute, Beijing University of Technology, Beijing 100124, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaInformation Technology Institute, Beijing University of Technology, Beijing 100124, ChinaInformation Technology Institute, Beijing University of Technology, Beijing 100124, ChinaInformation Technology Institute, Beijing University of Technology, Beijing 100124, ChinaMalicious social media bots are disseminators of malicious information on social networks and seriously affect information security and the network environment. Efficient and reliable classification of social media bots is crucial for detecting information manipulation in social networks. Aiming to correct the defects of high-cost labeling and unbalanced positive and negative samples in the existing methods of social media bot detection, and to reduce the training of abnormal samples in the model, we propose an anomaly detection framework based on a combination of a Variational AutoEncoder and an anomaly detection algorithm. The purpose is to use Variational AutoEncoder to automatically encode and decode sample features. The normal sample features are more similar to the initial features after decoding; however, there is a difference between the abnormal samples and the initial features. The decoding representation and the original features are combined, and then the anomaly detection method is used for detection. The results show that the area under the curve of the proposed model for identifying social media bots reaches 98% through the experiments on public datasets, which can effectively distinguish bots from common users and further verify the performance of the proposed model.https://www.mdpi.com/2076-3417/11/12/5482social networkssocial media bot detectionanomaly detectionVariational AutoEncoder |
| spellingShingle | Xiujuan Wang Qianqian Zheng Kangfeng Zheng Yi Sui Siwei Cao Yutong Shi Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor Applied Sciences social networks social media bot detection anomaly detection Variational AutoEncoder |
| title | Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor |
| title_full | Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor |
| title_fullStr | Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor |
| title_full_unstemmed | Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor |
| title_short | Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor |
| title_sort | detecting social media bots with variational autoencoder and k nearest neighbor |
| topic | social networks social media bot detection anomaly detection Variational AutoEncoder |
| url | https://www.mdpi.com/2076-3417/11/12/5482 |
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