[论文解读] LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognition
本文提出一种领域无关的 LSTM-CRF 方法用于网络安全命名实体识别，不依赖手工特征，在足够规模的标注语料上优于当前最先进的方法。
The automated and timely conversion of cybersecurity information from unstructured online sources, such as blogs and articles to more formal representations has become a necessity for many applications in the domain nowadays. Named Entity Recognition (NER) is one of the early phases towards this goal. It involves the detection of the relevant domain entities, such as product, version, attack name, etc. in technical documents. Although generally considered a simple task in the information extraction field, it is quite challenging in some domains like cybersecurity because of the complex structure of its entities. The state of the art methods require time-consuming and labor intensive feature engineering that describes the properties of the entities, their context, domain knowledge, and linguistic characteristics. The model demonstrated in this paper is domain independent and does not rely on any features specific to the entities in the cybersecurity domain, hence does not require expert knowledge to perform feature engineering. The method used relies on a type of recurrent neural networks called Long Short-Term Memory (LSTM) and the Conditional Random Fields (CRFs) method. The results we obtained showed that this method outperforms the state of the art methods given an annotated corpus of a decent size.
研究动机与目标
- 将来自非结构化在线信息的网络安全信息自动化并加快转换为正式表示。
- 解决网络安全中实体具有复杂结构和领域特定线索的命名实体识别挑战。
- 开发一个不需要针对网络安全的专家特征工程的模型。
- 证明神经序列标注方法在没有领域特定特征的情况下也能取得较强的性能。
提出的方法
- 使用长短时记忆 (LSTM) 循环神经网络进行序列标注。
- 将 LSTM 与条件随机场 (CRF) 结合用于实体的结构化预测。
- 避免任何针对网络安全实体的定制特征；依赖领域无关的表示。
- 在标注语料上进行训练并与最先进方法进行评估。
- 在给定足够大的标注数据集的情况下，证明该方法优于基线方法。
实验结果
研究问题
- RQ1在没有领域特定特征工程的情况下，LSTM-CRF 模型是否能够有效执行网络安全命名实体识别？
- RQ2在提供一个相当规模的标注语料时，领域无关的神经方法是否能够在网络安全 NER 上超越现有最先进的方法？
- RQ3标注数据量在多大程度上影响 LSTM-CRF 在网络安全 NER 上的性能提升？
主要发现
- 在给定一个相当规模的标注语料时，该方法优于最先进的方法。
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