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In 24th USENIX Security Symposium (USENIX Security 15). 563\u2013578."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3341183"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505581"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409702"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660282"},{"key":"e_1_3_2_1_54_1","volume-title":"22nd USENIX Security Symposium (USENIX Security 13)","author":"Thomas Kurt","year":"2013","unstructured":"Kurt Thomas , Damon McCoy , Chris Grier , Alek Kolcz , and Vern Paxson . 2013 . $Trafficking$ Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse . In 22nd USENIX Security Symposium (USENIX Security 13) . 195\u2013210. Kurt Thomas, Damon McCoy, Chris Grier, Alek Kolcz, and Vern Paxson. 2013. $Trafficking$ Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse. In 22nd USENIX Security Symposium (USENIX Security 13). 195\u2013210."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/1851275.1851226"},{"key":"e_1_3_2_1_56_1","volume-title":"22nd USENIX Security Symposium (USENIX Security 13)","author":"Wang Gang","year":"2013","unstructured":"Gang Wang , Tristan Konolige , Christo Wilson , Xiao Wang , Haitao Zheng , and Ben Y Zhao . 2013 . You are how you click: Clickstream analysis for sybil detection . In 22nd USENIX Security Symposium (USENIX Security 13) . 241\u2013256. Gang Wang, Tristan Konolige, Christo Wilson, Xiao Wang, Haitao Zheng, and Ben Y Zhao. 2013. You are how you click: Clickstream analysis for sybil detection. In 22nd USENIX Security Symposium (USENIX Security 13). 241\u2013256."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.26599\/BDMA.2019.9020001"},{"key":"e_1_3_2_1_58_1","unstructured":"wikipedia. [n. d.]. ADSL. https:\/\/en.wikipedia.org\/wiki\/Asymmetric_digital_subscriber_line \t\t\t\t  wikipedia. [n. d.]. ADSL. https:\/\/en.wikipedia.org\/wiki\/Asymmetric_digital_subscriber_line"},{"key":"e_1_3_2_1_59_1","unstructured":"wikipedia. [n. d.]. CAPTCHA. https:\/\/en.wikipedia.org\/wiki\/CAPTCHA \t\t\t\t  wikipedia. [n. d.]. CAPTCHA. https:\/\/en.wikipedia.org\/wiki\/CAPTCHA"},{"key":"e_1_3_2_1_60_1","unstructured":"wikipedia. [n. d.]. Internet_Water_Army. https:\/\/en.wikipedia.org\/wiki\/Internet_Water_Army \t\t\t\t  wikipedia. [n. d.]. Internet_Water_Army. https:\/\/en.wikipedia.org\/wiki\/Internet_Water_Army"},{"key":"e_1_3_2_1_61_1","unstructured":"wikipedia. [n. d.]. Man-in-the-middle attack. https:\/\/en.wikipedia.org\/wiki\/Man-in-the-middle_attack \t\t\t\t  wikipedia. [n. d.]. Man-in-the-middle attack. https:\/\/en.wikipedia.org\/wiki\/Man-in-the-middle_attack"},{"key":"e_1_3_2_1_62_1","unstructured":"wikipedia. [n. d.]. 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