FineCTI: A Framework for Mining Fine-grained Cyber Threat Information from Twitter Using NER Model
Chunyan Ma, Jun Jiang, Kai Zhang, Zhengwei Jiang, Peian Yang, Xuren Wang, Huamin Feng · 2023
To timely respond to cyber threats related to a specific IT infrastructure called fine-grained (e.g., Windows or Linux), security analysts need to require timely and comprehensive threat information. Twitter, as a vital source of real-time threat information, provides abundant but overwhelming information due to the increased data sources. Automatically mining and summarizing fine-grained threat information from Twitter can help security analysts maintain the infrastructure’s security. Most existing studies focus on classification, which carries less threat information. Some works use clustering based on text similarity relying on the embedding of text obtained from pre-trained models, which cannot be applied to short text, resulting in noisy clusters. Several works build topic models. However, the incoherent topic keywords are difficult to understand and analyze. To overcome these challenges, we design a FineCTI framework to mine the threat information related to the specific infrastructure on Twitter and generate a detailed threat information summary that is machine-readable and human-readable, efficiently reducing information overload. FineCTI optimizes the feature extraction part based on the named entity recognition model and performs clustering based on features extracted, thus effectively reducing the influence of sparsity of tweets on the clustering result and with the V-measure score improved by 7%. The cluster analysis results show that we can mine the fine-grained threats up to 15 days before the official disclosure date.