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DeepScan: Exploiting Deep Learning Malicious Account Detection in Location-Based Social Networks
Gong, Qingyuan1,2; Chen, Yang1,2; He, Xinlei1,2; Zhuang, Zhou1,2; Wang, Tianyi3; Huang, Hong4,5; Wang, Xin1,2; Fu, Xiaoming5
Source PublicationIEEE COMMUNICATIONS MAGAZINE
ISSN0163-6804
2018-11-01
Volume56Issue:11Pages:21-27
Corresponding AuthorGong, Qingyuan(qgong12@fudan.edu.cn)
AbstractOur 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.
DOI10.1109/MCOM.2018.1700575
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China ; Natural Science Foundation of Shanghai ; Shanghai Pujiang Program ; EU FP7 IRSES MobileCloud project ; Lindemann Foundation
WOS Research AreaEngineering ; Telecommunications
WOS SubjectEngineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000450603000004
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22834
Collection智能感知与计算研究中心
Corresponding AuthorGong, Qingyuan
Affiliation1.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
Recommended Citation
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.
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