DeepScan: Exploiting Deep Learning Malicious Account Detection in Location-Based Social Networks

doi:10.1109/MCOM.2018.1700575

CASIA OpenIR > 智能感知与计算研究中心

	DeepScan: Exploiting Deep Learning Malicious Account Detection in Location-Based Social Networks
	Gong, Qingyuan 1,2; Chen, Yang 1,2; He, Xinlei 1,2; Zhuang, Zhou 1,2; Wang, Tianyi3 ; Huang, Hong 4,5; Wang, Xin 1,2; Fu, Xiaoming 5
发表期刊	IEEE COMMUNICATIONS MAGAZINE
ISSN	0163-6804
	2018-11-01
卷号	56 期号:11 页码:21-27
通讯作者	Gong, Qingyuan(qgong12@fudan.edu.cn)
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.
DOI	10.1109/MCOM.2018.1700575
收录类别	SCI
语种	英语
资助项目	Lindemann Foundation[12-2016] ; EU FP7 IRSES MobileCloud project[612212] ; Shanghai Pujiang Program[16PJ1400700] ; Natural Science Foundation of Shanghai[16ZR1402200] ; National Natural Science Foundation of China[61472423] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[71731004] ; National Natural Science Foundation of China[61602122] ; 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]
项目资助者	National Natural Science Foundation of China ; Natural Science Foundation of Shanghai ; Shanghai Pujiang Program ; EU FP7 IRSES MobileCloud project ; Lindemann Foundation
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000450603000004
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：44[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/22834
专题	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.