Characterizing and detecting malicious crowdsourcing
Characterizing and detecting malicious crowdsourcing
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DOI：
10.1145/2486001.2491719
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
Tianyi Wang;G. Wang;Xing Li;Haitao Zheng;Ben Y. Zhao
中科院分区：
文献类型：
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作者：
Tianyi Wang;G. Wang;Xing Li;Haitao Zheng;Ben Y. Zhao
近年来流行的互联网服务表明，利用群众的力量可以取得令人瞩目的成就。然而，众包系统也对现有的用于保护互联网服务的安全机制构成了真正的挑战，特别是那些通过检测自动程序(如验证码)的活动来识别恶意活动的工具。在这项工作中，我们利用对两个大型拥挤草坪运动网站的访问来收集由拥挤草坪运动产生的大量地面事实数据。我们将这些数据与普通用户生成的“有机”内容进行比较和对比，以确定用于实时检测器的独特特征和潜在签名。这张海报描述了针对新浪微博系统的众筹活动采取的第一步。我们描述了我们的方法，我们的数据(超过29万个活动，3.4万个员工账户，6100万条推文...)，以及一些初步结果。
Popular Internet services in recent years have shown that remarkable things can be achieved by harnessing the power of the masses. However, crowd-sourcing systems also pose a real challenge to existing security mechanisms deployed to protect Internet services, particularly those tools that identify malicious activity by detecting activities of automated programs such as CAPTCHAs. In this work, we leverage access to two large crowdturfing sites to gather a large corpus of ground-truth data generated by crowdturfing campaigns. We compare and contrast this data with "organic" content generated by normal users to identify unique characteristics and potential signatures for use in real-time detectors. This poster describes first steps taken focused on crowdturfing campaigns targeting the Sina Weibo microblogging system. We describe our methodology, our data (over 290K campaigns, 34K worker accounts, 61 million tweets...), and some initial results.