Smoke Screener or Straight Shooter: Detecting Elite Sybil Attacks in User-Review Social Networks
作者： Haojin Zhu , Xiaohui Liang , Minhui Xue , Keith Ross , Shuang Hao
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摘要: Popular User-Review Social Networks (URSNs)---such as Dianping, Yelp, and Amazon---are often the targets of reputation attacks in which fake reviews are posted order to boost or diminish ratings listed products services. These emanate from a collection accounts, called Sybils, collectively managed by group real users. A new advanced scheme, we term elite Sybil attacks, recruits organically highly-rated accounts generate seemingly-trustworthy realistic-looking reviews. taken together form large-scale sparsely-knit network for existing fake-review defense systems unlikely succeed. In this paper, conduct first study define, characterize, detect attacks. We show that contemporary have hybrid architecture, with tier recruiting workers distributing tasks organizers, second posting profit workers. design ElsieDet, three-stage detection separates out suspicious groups users, then identifies campaign windows, finally users participating campaigns. perform empirical on ten million far most popular URSN service China. Our results more spread temporally, craft convincing reviews, higher filter bypass rates. also measure impact campaigns various industries (such cinemas, hotels, restaurants) well chain stores, demonstrate monitoring over time can provide valuable early alerts against
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