在全球流行病中应对假新闻传播的击键生物识别技术
Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic
摘要：
这项工作提出并分析了使用生物识别技术进行内容去匿名化。假新闻已经成为操纵公众舆论的有力工具，特别是在重大事件期间。特别是，COVID-19疫情期间假新闻的大规模传播迫使政府和公司打击错误信息。在这种情况下，能够将在互联网上传播此类恶意内容的多个账户或配置文件联系起来，同时隐藏在匿名状态下，将有助于主动识别和列入黑名单。在这场斗争中，行为生物识别技术可以成为强大的工具。在这项工作中，我们分析了生物特征识别的最新进展如何有助于在涉及10万名用户和100多万个类型序列的实验中将行为类型模式联系起来。我们提出的系统是基于递归神经网络适应内容去匿名化的背景下。假设的挑战，以链接的候选人配置文件池中的目标用户的类型的内容，我们的研究结果表明，可用于减少候选人配置文件的列表超过90%的识别。此外，当与辅助数据（如位置）相结合，我们的系统实现了一个Rank-1识别性能等于52.6%和10.9%的背景候选人名单组成的1 K和100 K配置文件，分别。
英文摘要：
This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
