Tracking Users on the Internet with Behavioral Patterns: Evaluation of Its Practical Feasibility
Tracking Users on the Internet with Behavioral Patterns: Evaluation of Its Practical Feasibility
复制标题
DOI：
10.1007/978-3-642-30436-1_20
复制
发表时间：
2012-06
期刊：
影响因子：
--
通讯作者：
Christian Banse;Dominik Herrmann;H. Federrath
中科院分区：
文献类型：
--
作者：
Christian Banse;Dominik Herrmann;H. Federrath
传统上，想要跟踪互联网用户活动的服务提供商依赖于HTTP cookie等显式跟踪技术。从隐私的角度来看，基于行为的跟踪甚至更危险，因为它允许服务提供商被动地跟踪用户，即。例如，没有饼干。在这种情况下，一个用户的多个会话被链接，利用从网络traffic.In本文中挖掘的特征模式，我们研究的可行性，基于行为的跟踪在现实世界中的设置，这是未知的。原则上，基于行为的跟踪可以由任何可以观察互联网上用户活动的攻击者执行。我们设计并实现了一个基于行为的跟踪技术，由一个朴素贝叶斯分类器支持的余弦相似性决策引擎。我们使用一个大规模的数据集来评估我们的技术，该数据集包含平均每天有2100多个并发用户使用的DNS解析器接收的所有查询。我们的技术是能够正确地连接88.2%的冲浪会话的日常基础上。我们还讨论了各种对策，降低我们的技术的有效性。
Traditionally, service providers, who want to track the activities of Internet users, rely on explicit tracking techniques like HTTP cookies. From a privacy perspectivebehavior-based trackingis even more dangerous, because it allows service providers to track users passively, i. e., without cookies. In this case multiple sessions of a user are linked by exploiting characteristic patterns mined from network traffic.In this paper we study thefeasibilityof behavior-based tracking in a real-world setting, which is unknown so far. In principle, behavior-based tracking can be carried out by any attacker that can observe the activities of users on the Internet. We design and implement a behavior-based tracking technique that consists of a Naive Bayes classifier supported by a cosine similarity decision engine. We evaluate our technique using a large-scale dataset that contains all queries received by a DNS resolver that is used by more than 2100 concurrent users on average per day. Our technique is able to correctly link 88.2 % of the surfing sessions on a day-to-day basis. We also discuss various countermeasures that reduce the effectiveness of our technique.