Multi-features based Semantic Augmentation Networks for Named Entity Recognition in Threat Intelligence. (arXiv:2207.00232v1 [cs.CR])

Extracting cybersecurity entities such as attackers and vulnerabilities from
unstructured network texts is an important part of security analysis. However,
the sparsity of intelligence data resulted from the higher frequency variations
and the randomness of cybersecurity entity names makes it difficult for current
methods to perform well in extracting security-related concepts and entities.
To this end, we propose a semantic augmentation method which incorporates
different linguistic features to enrich the representation of input tokens to
detect and classify the cybersecurity …

arxiv augmentation features intelligence networks semantic threat intelligence