Distant Supervision for Relations Extraction via Deep Residual Learning and Multi-instance Attention in Cybersecurity

A large number of open source threat intelligence resources provide regularly updated threat sources that can be applied to a variety of security analysis solutions. Fragmented security news, security forums, and vulnerability information are important sources of cyber threat intelligence, but it is difficult to correlate these multiple-source data. Cybersecurity knowledge graph is a powerful tool for data-driven thread intelligence computing. Relation extraction is a very important task in construction of cybersecurity knowledge graph from unstructured data. In order to reduce the influence of noisy data in deep learning model, we propose a distant supervised relation extraction model ResPCNN-ATT based on deep residual convolutional neural network and attention mechanism. This method takes word vector and position vector of the word as input of the model, extracts semantic features of texts through the piecewise convolutional neural network model PCNN, achieves the learning effect of less noisy data and better extracts deep semantic features in sentenses by using deep residuals Compared with other models, the model proposed in this paper achieves higher accuracy than other models.