Knowledge triple extraction in cybersecurity with adversarial active learning - Tao LI; Yuanbo GUO; Ankang JU (Tongxin xuebao)
Aiming at the problem that using pipeline methods for extracting cybersecurity knowledge triples may cause the errors propagation of entity recognition and did not consider the correlation between entity recognition and relation extraction,and training triple extraction model lacked labeled corpora,an end-to-end cybersecurity knowledge triple extraction method with adversarial active learning was proposed.For knowledge triple extraction,the conventional entity recognition and relation extraction were modelled as sequence labeling task through joint labeling strategy firstly.And then,a BiLSTM-LSTM-based model with dynamic attention mechanism was designed to jointly extract entities and relations,forming triples.Finally,with adversarial learning framework,a discriminator was trained to incrementally select high-quality samples for labeling,and the performance of the joint extraction model was continuously enhanced by iterative retraining.Experiments show that the proposed joint extraction model outperforms the existing cybersecurity knowledge triple extraction methods,and demonstrate the effectiveness of proposed adversarial active learning scheme.