论文标题
协作域阻止：使用联合NLP检测恶意域
Collaborative Domain Blocking: Using federated NLP To Detect Malicious Domains
论文作者
论文摘要
当前的内容过滤和阻止方法容易受到各种规避技术的影响，并且在处理新威胁方面相对较慢。这是由于这些方法使用浅模式识别的方法，该方法基于众包块列表中发现的正则表达规则。我们提出了一个新型系统，旨在通过研究与正在与之相互作用的域相关的网络内容的深层文本模式来纠正上述问题。此外，我们建议使用联合学习，使用户可以利用彼此的本地化知识/经验，这些知识/经验在不损害隐私的情况下应在网络上阻止什么或不应阻止什么。我们的实验表明了我们在现实世界中提出的方法的希望。我们还提供有关如何最好地实施建议系统的数据驱动建议。
Current content filtering and blocking methods are susceptible to various circumvention techniques and are relatively slow in dealing with new threats. This is due to these methods using shallow pattern recognition that is based on regular expression rules found in crowdsourced block lists. We propose a novel system that aims to remedy the aforementioned issues by examining deep textual patterns of network-oriented content relating to the domain being interacted with. Moreover, we propose to use federated learning that allows users to take advantage of each other's localized knowledge/experience regarding what should or should not be blocked on a network without compromising privacy. Our experiments show the promise of our proposed approach in real world settings. We also provide data-driven recommendations on how to best implement the proposed system.
