Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web Search

doi:10.1007/s11390-021-0218-2

	Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web Search
	Yang, Dong-Hui 1,2; Li, Zhen-Yu 1,2; Wang, Xiao-Hui 3; Salamatian, Kave 4; Xie, Gao-Gang 2,5
	2021-10-01
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN	1000-9000
卷号	36 期号:5 页码:1167-1183
摘要	Internet users heavily rely on web search engines for their intended information. The major revenue of search engines is advertisements (or ads). However, the search advertising suffers from fraud. Fraudsters generate fake traffic which does not reach the intended audience, and increases the cost of the advertisers. Therefore, it is critical to detect fraud in web search. Previous studies solve this problem through fraudster detection (especially bots) by leveraging fraudsters' unique behaviors. However, they may fail to detect new means of fraud, such as crowdsourcing fraud, since crowd workers behave in part like normal users. To this end, this paper proposes an approach to detecting fraud in web search from the perspective of fraudulent keywords. We begin by using a unique dataset of 150 million web search logs to examine the discriminating features of fraudulent keywords. Specifically, we model the temporal correlation of fraudulent keywords as a graph, which reveals a very well-connected community structure. Next, we design DFW (detection of fraudulent keywords) that mines the temporal correlations between candidate fraudulent keywords and a given list of seeds. In particular, DFW leverages several refinements to filter out non-fraudulent keywords that co-occur with seeds occasionally. The evaluation using the search logs shows that DFW achieves high fraud detection precision (99%) and accuracy (93%). A further analysis reveals several typical temporal evolution patterns of fraudulent keywords and the co-existence of both bots and crowd workers as fraudsters for web search fraud.
关键词	community structure fraud analysis fraudulent keyword detection web search
DOI	10.1007/s11390-021-0218-2
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFB1800205] ; National Natural Science Foundation of China[61725206] ; National Natural Science Foundation of China[U20A20180] ; National Natural Science Foundation of China[GJHZ202114]
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号	WOS:000712575700016
出版者	SCIENCE PRESS
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16934
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Zhen-Yu
作者单位	1.Chinese Acad Sci, Network Technol Res Ctr, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Global Energy Interconnect Res Inst Co Ltd, Beijing 102209, Peoples R China 4.Univ Savoie Mt Blanc, LISTIC Lab Comp Sci Syst Informat & Knowledge Pro, F-73011 Chambery, France 5.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Dong-Hui,Li, Zhen-Yu,Wang, Xiao-Hui,et al. Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web Search[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2021,36(5):1167-1183.
APA	Yang, Dong-Hui,Li, Zhen-Yu,Wang, Xiao-Hui,Salamatian, Kave,&Xie, Gao-Gang.(2021).Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web Search.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,36(5),1167-1183.
MLA	Yang, Dong-Hui,et al."Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web Search".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 36.5(2021):1167-1183.