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A reputation framework for behavioural history : developing and sharing reputations from behavioural history of network clients

机译：行为历史的声誉框架：从网络客户的行为历史中开发和分享声誉

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摘要

The open architecture of the Internet has enabled its massive growth and success by facilitating easy connectivity between hosts. At the same time, the Internet has also opened itself up to abuse, e.g. arising out of unsolicited communication, both intentional and unintentional. It remains an open question as to how best servers should protect themselves from malicious clients whilst offering good service to innocent clients. There has been research on behavioural profiling and reputation of clients, mostly at the network level and also for email as an application, to detect malicious clients. However, this area continues to pose open research challenges. This thesis is motivated by the need for a generalised framework capable of aiding efficient detection of malicious clients while being able to reward clients with behaviour profiles conforming to the acceptable use and other relevant policies. The main contribution of this thesis is a novel, generalised, context-aware, policy independent, privacy preserving framework for developing and sharing client reputation based on behavioural history. The framework, augmenting existing protocols, allows fitting in of policies at various stages, thus keeping itself open and flexible to implementation. Locally recorded behavioural history of clients with known identities are translated to client reputations, which are then shared globally. The reputations enable privacy for clients by not exposing the details of their behaviour during interactions with the servers. The local and globally shared reputations facilitate servers in selecting service levels, including restricting access to malicious clients. We present results and analyses of simulations, with synthetic data and some proposed example policies, of client-server interactions and of attacks on our model. Suggestions presented for possible future extensions are drawn from our experiences with simulation.
机译：互联网的开放架构通过促进主机之间的便捷连接，实现了其巨大的增长和成功。同时，互联网也开放了自己进行滥用行为的机会，例如由于有意和无意的未经请求的交流而产生的。最好的服务器应如何保护自己免受恶意客户端的侵害，同时又为无辜的客户端提供良好的服务，这仍然是一个悬而未决的问题。已经进行了有关客户端的行为分析和信誉的研究，主要是在网络级别上进行的，并且还针对电子邮件作为应用程序来检测恶意客户端。但是，这一领域继续带来开放研究挑战。本论文的动机是需要一种通用框架，该框架能够帮助有效检测恶意客户端，同时能够以符合可接受使用和其他相关策略的行为概况来奖励客户端。本文的主要贡献是基于行为历史开发和共享客户信誉的新颖，通用，上下文感知，策略独立，隐私保护的框架。该框架增强了现有协议，允许在各个阶段适应策略，从而保持开放性和灵活的实施性。具有已知身份的客户的本地记录的行为历史记录会转换为客户信誉，然后在全球范围内共享。信誉通过不与服务器交互期间公开其行为的详细信息来为客户端提供隐私。本地和全球共享的信誉有助于服务器选择服务级别，包括限制对恶意客户端的访问。我们使用合成数据和一些建议的示例策略，客户端-服务器交互以及对我们模型的攻击，提供了仿真结果和分析。提出的有关可能的未来扩展的建议是从我们的模拟经验中得出的。

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  • 作者

    Basu Anirban;

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  • 年度 2010
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  • 原文格式 PDF
  • 正文语种 English
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