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Kanei, D. Chiba, K. Hato, and M. Akiyama, \u201cPrecise and robust detection of advertising fraud,\u201d 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), pp.776-785, IEEE, 2019. 10.1109\/compsac.2019.00115","DOI":"10.1109\/COMPSAC.2019.00115"},{"key":"2","unstructured":"[2] \u201cThe Interactive Advertising Bureau | IAB internet advertising revenue report.\u201d https:\/\/www.iab.com\/wp-content\/uploads\/2019\/05\/Full-Year-2018-IAB-Internet-Advertising-Revenue-Report.pdf"},{"key":"3","unstructured":"[3] \u201cWhat Is An Untrustworthy Supply Chain Costing The U.S. Digital Advertising Industry?.\u201d https:\/\/www.iab.com\/wp-content\/uploads\/2015\/11\/IAB_EY_Report.pdf"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] Y. Chen, P. Kintis, M. Antonakakis, Y. Nadji, D. Dagon, W. Lee, and M. Farrell, \u201cFinancial lower bounds of online advertising abuse,\u201d Proc. 13th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA &apos;16, pp.231-254, Springer, 2016. 10.1007\/978-3-319-40667-1_12","DOI":"10.1007\/978-3-319-40667-1_12"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] Y. Chen, Y. Nadji, R. Romero-G\u00f3mez, M. Antonakakis, and D. Dagon, \u201cMeasuring network reputation in the ad-bidding process,\u201d Proc. 14th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA &apos;17, pp.388-409, Springer, 2017. 10.1007\/978-3-319-60876-1_18","DOI":"10.1007\/978-3-319-60876-1_18"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] J. Crussell, R. Stevens, and H. Chen, \u201cMadfraud: Investigating ad fraud in android applications,\u201d Proc. 12th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys &apos;14, pp.123-134, ACM, 2014. 10.1145\/2594368.2594391","DOI":"10.1145\/2594368.2594391"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] X. Xing, W. Meng, B. Lee, U. Weinsberg, A. Sheth, R. Perdisci, and W. Lee, \u201cUnderstanding malvertising through ad-injecting browser extensions,\u201d Proc. 24th International Conference on World Wide Web, WWW &apos;15, pp.1286-1295, International World Wide Web Conferences Steering Committee, 2015. 10.1145\/2736277.2741630","DOI":"10.1145\/2736277.2741630"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] V. Dave, S. Guha, and Y. Zhang, \u201cMeasuring and fingerprinting click-spam in ad networks,\u201d Proc. ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication, SIGCOMM &apos;12, pp.175-186, ACM, 2012. 10.1145\/2342356.2342394","DOI":"10.1145\/2342356.2342394"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] V. Dave, S. Guha, and Y. Zhang, \u201cViceROI: Catching click-spam in search ad networks,\u201d Proc. 2013 ACM SIGSAC Conference on Computer and Communications Security, CCS &apos;13, pp.765-776, ACM, 2013. 10.1145\/2508859.2516688","DOI":"10.1145\/2508859.2516688"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] T. Tian, J. Zhu, F. Xia, X. Zhuang, and T. Zhang, \u201cCrowd fraud detection in internet advertising,\u201d Proc. 24th International Conference on World Wide Web, WWW &apos;15, pp.1100-1110, International World Wide Web Conferences Steering Committee, 2015. 10.1145\/2736277.2741136","DOI":"10.1145\/2736277.2741136"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] B. Stone-Gross, R. Stevens, A. Zarras, R. Kemmerer, C. Kruegel, and G. Vigna, \u201cUnderstanding fraudulent activities in online ad exchanges,\u201d Proc. 2011 ACM SIGCOMM Conference on Internet Measurement Conference, IMC &apos;11, pp.279-294, ACM, 2011. 10.1145\/2068816.2068843","DOI":"10.1145\/2068816.2068843"},{"key":"12","unstructured":"[12] N. Daswani and M. Stoppelman, \u201cThe anatomy of clickbot.a,\u201d Proc. First Conference on First Workshop on Hot Topics in Understanding Botnets, HotBots&apos;07, pp.11-11, USENIX Association, 2007."},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] P. Pearce, V. Dave, C. Grier, K. Levchenko, S. Guha, D. McCoy, V. Paxson, S. Savage, and G.M. Voelker, \u201cCharacterizing large-scale click fraud in zeroaccess,\u201d Proc. 2014 ACM SIGSAC Conference on Computer and Communications Security, CCS &apos;14, pp.141-152, ACM, 2014. 10.1145\/2660267.2660369","DOI":"10.1145\/2660267.2660369"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] B. Miller, P. Pearce, C. Grier, C. Kreibich, and V. Paxson, \u201cWhat&apos;s clicking what? Techniques and innovations of today&apos;s clickbots,\u201d Proc. 8th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA&apos;11, pp.164-183, Springer, 2011. 10.1007\/978-3-642-22424-9_10","DOI":"10.1007\/978-3-642-22424-9_10"},{"key":"15","unstructured":"[15] N. Jagpal, E. Dingle, J. Gravel, P. Mavrommatis, N. Provos, M. Rajab, and K. Thomas, \u201cTrends and lessons from three years fighting malicious extensions,\u201d Proc. 24th USENIX Conference on Security Symposium, SEC&apos;15, pp.579-593, USENIX Association, 2015."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] K. Thomas, E. Bursztein, C. Grier, G. Ho, N. Jagpal, A. Kapravelos, D. Mccoy, A. Nappa, V. Paxson, P. Pearce, N. Provos, and M. Rajab, \u201cAd injection at scale: Assessing deceptive advertisement modifications,\u201d Proce. 2015 IEEE Symposium on Security and Privacy, SP &apos;15, pp.151-167, IEEE, 2015. 10.1109\/sp.2015.17","DOI":"10.1109\/SP.2015.17"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] A. Metwally, D. Agrawal, and A.E. Abbadi, \u201cDuplicate detection in click streams,\u201d Proc. 14th International Conference on World Wide Web, WWW &apos;05, pp.12-21, ACM, 2005. 10.1145\/1060745.1060753","DOI":"10.1145\/1060745.1060753"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] L. Zhang and Y. Guan, \u201cDetecting click fraud in pay-per-click streams of online advertising networks,\u201d Proc. 28th International Conference on Distributed Computing Systems, ICDCS &apos;08, pp.77-84, IEEE, 2008. 10.1109\/icdcs.2008.98","DOI":"10.1109\/ICDCS.2008.98"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] A. Metwally, D. Agrawal, and A.E. Abbadi, \u201cDetectives: Detecting coalition hit inflation attacks in advertising networks streams,\u201d Proc. 16th International Conference on World Wide Web, WWW &apos;07, pp.241-250, ACM, 2007. 10.1145\/1242572.1242606","DOI":"10.1145\/1242572.1242606"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] F. Yu, Y. Xie, and Q. Ke, \u201cSBotMiner: Large scale search bot detection,\u201d Proc. Third ACM International Conference on Web Search and Data Mining, WSDM &apos;10, pp.421-430, ACM, 2010. 10.1145\/1718487.1718540","DOI":"10.1145\/1718487.1718540"},{"key":"21","unstructured":"[21] \u201cMozilla Foundation | Public Suffix List.\u201d https:\/\/publicsuffix.org\/"},{"key":"22","unstructured":"[22] \u201cMaxMind | GeoIP2 Databases.\u201d https:\/\/www.maxmind.com\/en\/geoip2-databases"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] Y. Xie, F. Yu, K. Achan, E. Gillum, M. Goldszmidt, and T. Wobber, \u201cHow dynamic are IP addresses?,\u201d Proc. 2007 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, SIGCOMM &apos;07, pp.301-312, ACM, 2007. 10.1145\/1282380.1282415","DOI":"10.1145\/1282380.1282415"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] Y. Jin, E. Sharafuddin, and Z. Zhang, \u201cIdentifying dynamic IP address blocks serendipitously through background scanning traffic,\u201d Proc. 2007 ACM CoNEXT Conference, CoNEXT &apos;07, pp.4:1-4:12, ACM, 2007. 10.1145\/1364654.1364659","DOI":"10.1145\/1364654.1364659"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] X. Cai and J. Heidemann, \u201cUnderstanding block-level address usage in the visible internet,\u201d Proc. ACM SIGCOMM 2010 Conference, SIGCOMM &apos;10, pp.99-110, ACM, 2010. 10.1145\/1851182.1851196","DOI":"10.1145\/1851182.1851196"},{"key":"26","unstructured":"[26] \u201cAlexa Top Sites.\u201d https:\/\/www.alexa.com\/topsites"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] L. 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