Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (5): 1167-1183.doi: 10.1007/s11390-021-0218-2

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Networks and Distributed Computing

• Regular Paper • Previous Articles     Next Articles

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

Dong-Hui Yang1,2, Zhen-Yu Li1,2,*, Member, CCF, ACM, IEEE, Xiao-Hui Wang3, Kavé Salamatian4, and Gao-Gang Xie2,5, Member, CCF, ACM, IEEE        

  1. 1 Network Technology Research Center, Institute of Computing Technology, Chinese Academy of Sciences Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China;
    3 Global Energy Interconnection Research Institute Co., Ltd., Beijing 102209, China;
    4 LISTIC Laboratory of Computer Science, Systems, Information and Knowledge Processing, Université Savoie Mont Blanc, Chambéry 73011, France;
    5 Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2019-12-11 Revised:2021-07-01 Online:2021-09-30 Published:2021-09-30
  • About author:Dong-Hui Yang is currently a Ph.D. candidate at the Institute of Computing Technology (ICT), Chinese Academy Sciences (CAS), Beijing. He received his B.E. degree in Department of Automation from Tsinghua University, Beijing, in 2016. His research interests include the domain name system (DNS) and Internet measurement.
  • Supported by:
    This work was supported by the National Key Research and Development Program of China under Grant No. 2018YFB1800205, the National Natural Science Foundation of China under Grant Nos. 61725206 and U20A20180, and CAS-Austria Project under Grant No. GJHZ202114.

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.

Key words: community structure; fraud analysis; fraudulent keyword detection; web search;

[1] Jansen B J. Click fraud. Computer, 2007, 40(7):85-86. DOI:10.1109/MC.2007.232.
[2] Jansen B J, Mullen T. Sponsored search:An overview of the concept, history, and technology. International Journal of Electronic Business, 2008, 6(2):114-131.
[3] Ghose A, Yang S. An empirical analysis of search engine advertising:Sponsored search in electronic markets. Management Science, 2009, 55(10):1605-1622. DOI:10.1504/IJEB.2008.018068.
[4] Fain D C, Pedersen J O. Sponsored search:A brief history. Bulletin of the American Society for Information Science and Technology, 2006, 32(2):12-13. DOI:10.1002/bult.1720320206.
[5] Gallaugher J. Information Systems:A Manager's Guide to Harnessing Technology. University of Minnesota Libraries Publishing, 2015.
[6] Shakiba T, Zarifzadeh S, Derhami V. Spam query detection using stream clustering. World Wide Web, 2018, 21(2):557-572. DOI:10.1007/s11280-017-0471-z.
[7] Stone-Gross B, Stevens R, Zarras A, Kemmerer R, Kruegel C, Vigna G. Understanding fraudulent activities in online ad exchanges. In Proc. the 2011 ACM SIGCOMM Internet Measurement Conference, November 2011, pp.279-294. DOI:10.1145/2068816.2068843.
[8] Pandit S, Chau D H, Wang S, Faloutsos C. Netprobe:A fast and scalable system for fraud detection in online auction networks. In Proc. the 16th International Conference on World Wide Web, May 2007, pp.201-210. DOI:10.1145/1242572.1242600.
[9] Yu F, Xie Y L, Ke Q F. SBotMiner:Large scale search bot detection. In Proc. the 3rd ACM International Conference on Web Search and Data Mining, February 2010, pp.421-430. DOI:10.1145/1718487.1718540.
[10] Zhang J J, Xie Y L, Yu F, Soukal D, Lee W. Intention and origination:An inside look at large-scale bot queries. In Proc. the 20th Annual Network and Distributed System Security Symposium, February 2013.
[11] Zhang L F, Guan Y. Detecting click fraud in pay-per-click streams of online advertising networks. In Proc. the 28th International Conference on Distributed Computing Systems, June 2008, pp.77-84. DOI:10.1109/ICDCS.2008.98.
[12] Buehrer G, Stokes J W, Chellapilla K, Platt J. Classification of automated web traffic. In Weaving Services and People on the World Wide Web, King I, Baeza-Yates R (eds.), Springer Verlag, 2009, pp.3-26. DOI:10.1007/978-3-642-00570-11.
[13] Sadagopan N, Li J. Characterizing typical and atypical user sessions in clickstreams. In Proc. the 17th International Conference on World Wide Web, April 2008, pp.885-894. DOI:10.1145/1367497.1367617.
[14] Duskin O, Feitelson D G. Distinguishing humans from robots in web search logs:Preliminary results using query rates and intervals. In Proc. the 2009 Workshop on Web Search Click Data, February 2009, pp.15-19. DOI:10.1145/1507509.1507512.
[15] Tian T, Zhu J, Xia F, Zhuang X, Zhang T. Crowd fraud detection in Internet advertising. In Proc. the 24th International Conference on World Wide Web, May 2015, pp.1100-1110. DOI:10.1145/2736277.2741136.
[16] Kang H W, Wang K S, Soukal D, Behr F, Zheng Z J. Largescale bot detection for search engines. In Proc. the 19th International Conference on World Wide Web, April 2010, pp.501-510. DOI:10.1145/1772690.1772742.
[17] Haidar R, Elbassuoni S. Website navigation behavior analysis for bot detection. In Proc. the 2017 IEEE International Conference on Data Science and Advanced Analytics, October 2017, pp.60-68. DOI:10.1109/DSAA.2017.13.
[18] Guo Y, Shi J, Cao Z, Kang C, Xiong G, Li Z. Machine learning based cloudbot detection using multi-layer traffic statistics. In Proc. the 21st IEEE International Conference on High Performance Computing and Communications, the 17th IEEE International Conference on Smart City and the 5th IEEE International Conference on Data Science and Systems, August 2019, pp.2428-2435. DOI:10.1109/HPCC/SmartCity/DSS.2019.00339.
[19] Toffalini F, Abbà M, Carra D, Balzarotti D. Google dorks:Analysis, creation, and new defenses. In Proc. the 13th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, July 2016, pp.255-275. DOI:10.1007/978-3-319-40667-113.
[20] Metwally A, Agrawal D, Abbadi A E. Duplicate detection in click streams. In Proc. the 14th International Conference on World Wide Web, May 2005, pp.12-21. DOI:10.1145/1060745.1060753.
[21] Metwally A, Agrawal D, Abbadi A E. Detectives:Detecting coalition hit inflation attacks in advertising networks streams. In Proc. the 16th International Conference on World Wide Web, May 2007, pp.241-250. DOI:10.1145/1242572.1242606.
[22] Immorlica N, Jain K, Mahdian M, Talwar K. Click fraud resistant methods for learning click-through rates. In Proc. the 1st International Workshop on Internet and Network Economics, December 2005, pp.34-45. DOI:10.1007/116009305.
[23] Dave V, Guha S, Zhang Y. ViceROI:Catching clickspam in search ad networks. In Proc. the 2013 ACM SIGSAC Conference on Computer & Communications Security, November 2013, pp.765-776. DOI:10.1145/2508859.2516688.
[24] Li X, Zhang M, Liu Y Q, Ma S P, Jin Y J, Ru L Y. Search engine click spam detection based on bipartite graph propagation. In Proc. the 7th ACM International Conference on Web Search and Data Mining, February 2014, pp.93-102. DOI:10.1145/2556195.2556214.
[25] Nagaraja S, Shah R. Clicktok:Click fraud detection using traffic analysis. In Proc. the 12th Conference on Security and Privacy in Wireless and Mobile Networks, May 2019, pp.105-116. DOI:10.1145/3317549.3323407.
[26] DeBlasio J, Guha S, Voelker G M, Snoeren A C. Exploring the dynamics of search advertiser fraud. In Proc. the 2017 Internet Measurement Conference, November 2017, pp.157-170. DOI:10.1145/3131365.3131393.
[27] Wei C, Liu Y Q, Zhang M, Ma S P, Ru L Y, Zhang K. Fighting against web spam:A novel propagation method based on click-through data. In Proc. the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2012, pp.395-404. DOI:10.1145/2348283.2348338.
[28] Haider C M R, Iqbal A, Rahman A H, Rahman M S. An ensemble learning based approach for impression fraud detection in mobile advertising. Journal of Network and Computer Applications, 2018, 112:126-141. DOI:10.1016/j.jnca.2018.02.021.
[29] Dong F, Wang H Y, Li L, Guo Y, Bissyandé T F, Liu T M, Xu G A, Klein J. FraudDroid:Automated ad fraud detection for Android apps. In Proc. the 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, November 2018, pp.257-268. DOI:10.1145/3236024.3236045.
[30] Halfaker A, Keyes O, Kluver D, Thebault-Spieker J, Nguyen T, Shores K, Uduwage A, Warncke-Wang M. User session identification based on strong regularities in interactivity time. In Proc. the 24th International Conference on World Wide Web, May 2015, pp.410-418. DOI:10.1145/2736277.2741117.
[31] Jones R, Klinkner K L. Beyond the session timeout:Automatic hierarchical segmentation of search topics in query logs. In Proc. the 17th ACM Conference on Information and Knowledge Management, October 2008, pp.699-708. DOI:10.1145/1458082.1458176.
[32] Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 1974, 19(6):716-723. DOI:10.1109/TAC.1974.1100705.
[33] Fruchterman T M J, Reingold E M. Graph drawing by forcedirected placement. Software:Practice and Experience, 1991, 21(11):1129-1164. DOI:10.1002/spe.4380211102.
[34] Saram?ki J, Kivel? M, Onnela J P, Kaski K, Kertesz J. Generalizations of the clustering coefficient to weighted complex networks. Physical Review E, 2007, 75(2):Article No. 027105. DOI:10.1103/PhysRevE.75.027105.
[35] Schütze H, Manning C D, Raghavan P. Introduction to Information Retrieval. Cambridge University Press, 2008.
[36] Pelleg D, Moore A. X-means:Extending K-means with efficient estimation of the number of clusters. In Proc. the 17th International Conference on Machine Learning, June 29-July 2, 2000, pp.727-734.
[37] Onnela J P, Saram?ki J, Hyv?nen J, Szabó G, Lazer D, Kaski K, Kertész J, Barabási A L. Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences, 2007, 104(18):7332-7336. DOI:10.1073/pnas.0610245104.
[38] Whang J J, Jung Y, Kang S, Yoo D, Dhillon I S. Scalable Anti-TrustRank with qualified site-level seeds for link-based web spam detection. In Proc. the 2020 Web Conference, April 2020, pp.593-602. DOI:10.1145/3366424.3385773.
[39] Wang H D, Xu F L, Li Y, Zhang P Y, Jin D P. Understanding mobile traffic patterns of large scale cellular towers in urban environment. In Proc. the 2015 Internet Measurement Conference, October 2015, pp.225-238. DOI:10.1145/2815675.2815680.
[40] Corpet F. Multiple sequence alignment with hierarchical clustering. Nucleic Acids Research, 1988, 16(22):10881-10890. DOI:10.1093/nar/16.22.10881.
[41] Davies D L, Bouldin D W. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1979, 1(2):224-227. DOI:10.1109/TPAMI.1979.4766909.
[42] Maulik U, Bandyopadhyay S. Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(12):1650-1654. DOI:10.1109/TPAMI.2002.1114856.
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[1] Zhou Di;. A Recovery Technique for Distributed Communicating Process Systems[J]. , 1986, 1(2): 34 -43 .
[2] Li Wei;. A Structural Operational Semantics for an Edison Like Language(2)[J]. , 1986, 1(2): 42 -53 .
[3] Chen Shihua;. On the Structure of Finite Automata of Which M Is an(Weak)Inverse with Delay τ[J]. , 1986, 1(2): 54 -59 .
[4] Li Wanxue;. Almost Optimal Dynamic 2-3 Trees[J]. , 1986, 1(2): 60 -71 .
[5] Feng Yulin;. Recursive Implementation of VLSI Circuits[J]. , 1986, 1(2): 72 -82 .
[6] Liu Mingye; Hong Enyu;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[7] Wang Xuan; Lü Zhimin; Tang Yuhai; Xiang Yang;. A High Resolution Chinese Character Generator[J]. , 1986, 1(2): 1 -14 .
[8] C.Y.Chung; H.R.Hwa;. A Chinese Information Processing System[J]. , 1986, 1(2): 15 -24 .
[9] Sun Zhongxiu; Shang Lujun;. DMODULA:A Distributed Programming Language[J]. , 1986, 1(2): 25 -31 .
[10] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .

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