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Using Spammers' Computing Resources for Volunteer Computing.

机译:使用垃圾邮件制造者的计算资源进行志愿者计算。

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摘要

Spammers are continually looking to circumvent counter-measures seeking to slow them down. An immense amount of time and money is currently devoted to hiding spam, but not enough is devoted to effectively preventing it. One approach for preventing spam is to force the spammer's machine to solve a computational problem of varying difficulty before granting access. The idea is that suspicious or problematic requests are given difficult problems to solve while legitimate requests are allowed through with minimal computation. Unfortunately, most systems that employ this model waste the computing resources being used, as they are directed towards solving cryptographic problems that provide no societal benefit. While systems such as reCAPTCHA and FoldIt have allowed users to contribute solutions to useful problems interactively, an analogous solution for non-interactive proof-of-work does not exist. Towards this end, this paper describes MetaCAPTCHA and reBOINC, an infrastructure for supporting useful proof-of-work that is integrated into a web spam throttling service. The infrastructure dynamically issues CAPTCHAs and proof-of-work puzzles while ensuring that malicious users solve challenging puzzles. Additionally, it provides a framework that enables the computational resources of spammers to be redirected towards meaningful research. To validate the efficacy of our approach, prototype implementations based on OpenCV and BOINC are described that demonstrate the ability to harvest spammer's resources for beneficial purposes.
机译:垃圾邮件发送者一直在寻找规避措施,以减慢其速度。当前,大量时间和金钱专用于隐藏垃圾邮件,但不足以有效地防止垃圾邮件。防止垃圾邮件的一种方法是强制垃圾邮件发送者的机器在授予访问权限之前解决各种难度的计算问题。这个想法是给可疑或有问题的请求提供难以解决的问题,而通过最少的计算就可以允许合法请求。不幸的是,大多数采用此模型的系统都在浪费正在使用的计算资源,因为它们直接用于解决不提供社会效益的密码问题。尽管诸如reCAPTCHA和FoldIt之类的系统已允许用户交互地为有用的问题提供解决方案,但不存在用于非交互性工作量证明的类似解决方案。为此,本文描述了MetaCAPTCHA和reBOINC,这是一种用于支持有用的工作量证明的基础结构,该基础结构已集成到Web垃圾邮件限制服务中。基础架构动态地发布验证码和工作量证明难题,同时确保恶意用户解决具有挑战性的难题。此外,它提供了一个框架,可使垃圾邮件发送者的计算资源重定向到有意义的研究。为了验证我们方法的有效性,描述了基于OpenCV和BOINC的原型实现,展示了为有益目的而收集垃圾邮件发送者资源的能力。

著录项

  • 作者

    Bui, Thai Le Quy.;

  • 作者单位

    Portland State University.;

  • 授予单位 Portland State University.;
  • 学科 Computer science.
  • 学位 M.S.
  • 年度 2014
  • 页码 90 p.
  • 总页数 90
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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