DeepScan: Exploiting Deep Learning Malicious Account Detection in Location-Based Social Networks
文献类型：期刊论文
| 作者 | Gong, Qingyuan1,2; Chen, Yang1,2 ; He, Xinlei1,2; Zhuang, Zhou1,2; Wang, Tianyi3; Huang, Hong4,5; Wang, Xin1,2; Fu, Xiaoming5
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| 刊名 | IEEE COMMUNICATIONS MAGAZINE
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| 出版日期 | 2018-11-01 |
| 卷号 | 56期号:11页码:21-27 |
| ISSN号 | 0163-6804 |
| DOI | 10.1109/MCOM.2018.1700575 |
| 通讯作者 | Gong, Qingyuan(qgong12@fudan.edu.cn) |
| 英文摘要 | Our daily lives have been immersed in wide-spread location-based social networks (LBSNs). As an open platform LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information often with the intention of influencing users' decisions in urban computing environments. To provide reliable information and improve the experience for legitimate users we design and implement DeepScan a malicious account detection system for LBSNs. Different from existing approaches DeepScan leverages emerging deep learning technologies to learn users' dynamic behavior. In particular we introduce the long short-term memory (LSTM) neural network to conduct time series analysis of user activities. DeepScan combines newly introduced time series features and a set of conventional features extracted from user activities and exploits a supervised machine-learning-based model for detection. Using real traces collected from Dianping a representative LBSN we demonstrate that DeepScan can achieve excellent prediction performance with an F1-score of 0.964. We also find that the time series features play a critical role in the detection system. |
| 资助项目 | National Natural Science Foundation of China[61602122] ; National Natural Science Foundation of China[71731004] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61472423] ; Natural Science Foundation of Shanghai[16ZR1402200] ; Shanghai Pujiang Program[16PJ1400700] ; EU FP7 IRSES MobileCloud project[612212] ; Lindemann Foundation[12-2016] |
| WOS研究方向 | Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000450603000004 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Shanghai ; Shanghai Pujiang Program ; EU FP7 IRSES MobileCloud project ; Lindemann Foundation |
| 源URL | [http://ir.ia.ac.cn/handle/173211/22833]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Gong, Qingyuan |
| 作者单位 | 1.Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China 2.Xidian Univ, State Key Lab Integrated Serv Networks, Xian, Shaanxi, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing Bytedance Technol & Res Ctr Precis Sening, Beijing, Peoples R China 4.Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Hubei, Peoples R China 5.Univ Gottingen, Inst Comp Sci, Gottingen, Germany |
| 推荐引用方式 GB/T 7714 | Gong, Qingyuan,Chen, Yang,He, Xinlei,et al. DeepScan: Exploiting Deep Learning Malicious Account Detection in Location-Based Social Networks[J]. IEEE COMMUNICATIONS MAGAZINE,2018,56(11):21-27. |
| APA | Gong, Qingyuan.,Chen, Yang.,He, Xinlei.,Zhuang, Zhou.,Wang, Tianyi.,...&Fu, Xiaoming.(2018).DeepScan: Exploiting Deep Learning Malicious Account Detection in Location-Based Social Networks.IEEE COMMUNICATIONS MAGAZINE,56(11),21-27. |
| MLA | Gong, Qingyuan,et al."DeepScan: Exploiting Deep Learning Malicious Account Detection in Location-Based Social Networks".IEEE COMMUNICATIONS MAGAZINE 56.11(2018):21-27. |
入库方式： OAI收割
来源：自动化研究所
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