论文标题
智能命名实体的深度学习方法对网络安全的识别
Deep Learning Approach for Intelligent Named Entity Recognition of Cyber Security
论文作者
论文摘要
近年来，以非结构化文本的形式产生的网络安全数据数量，例如社交媒体资源，博客，文章等等。命名实体识别（NER）是将这些非结构化数据转换为结构化数据的第一步，许多应用程序可以使用。网络安全数据NER的现有方法基于规则和语言特征。本文提出了一种基于有条件的随机场（CRF）的基于深度学习（DL）的方法。评估了几种DL架构以找到最佳的架构。与公开可用的基准数据集中的其他各种DL框架相比，双向门控复发单元（BI-GRU），卷积神经网络（CNN）和CRF的组合表现更好。这可能是由于双向结构保留与未来和以前单词相关的特征的原因。
In recent years, the amount of Cyber Security data generated in the form of unstructured texts, for example, social media resources, blogs, articles, and so on has exceptionally increased. Named Entity Recognition (NER) is an initial step towards converting this unstructured data into structured data which can be used by a lot of applications. The existing methods on NER for Cyber Security data are based on rules and linguistic characteristics. A Deep Learning (DL) based approach embedded with Conditional Random Fields (CRFs) is proposed in this paper. Several DL architectures are evaluated to find the most optimal architecture. The combination of Bidirectional Gated Recurrent Unit (Bi-GRU), Convolutional Neural Network (CNN), and CRF performed better compared to various other DL frameworks on a publicly available benchmark dataset. This may be due to the reason that the bidirectional structures preserve the features related to the future and previous words in a sequence.
