@Article{cmc.2020.012023,
AUTHOR = {Tao Li, Yongjin Hu, Ankang Ju, Zhuoran Hu},
TITLE = {Adversarial Active Learning for Named Entity Recognition in Cybersecurity},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {66},
YEAR = {2021},
NUMBER = {1},
PAGES = {407--420},
URL = {http://www.techscience.com/cmc/v66n1/40455},
ISSN = {1546-2226},
ABSTRACT = {Owing to the continuous barrage of cyber threats, there is a massive
amount of cyber threat intelligence. However, a great deal of cyber threat intelligence come from textual sources. For analysis of cyber threat intelligence, many
security analysts rely on cumbersome and time-consuming manual efforts. Cybersecurity knowledge graph plays a significant role in automatics analysis of cyber
threat intelligence. As the foundation for constructing cybersecurity knowledge
graph, named entity recognition (NER) is required for identifying critical
threat-related elements from textual cyber threat intelligence. Recently, deep neural network-based models have attained very good results in NER. However, the
performance of these models relies heavily on the amount of labeled data. Since
labeled data in cybersecurity is scarce, in this paper, we propose an adversarial
active learning framework to effectively select the informative samples for further
annotation. In addition, leveraging the long short-term memory (LSTM) network
and the bidirectional LSTM (BiLSTM) network, we propose a novel NER model
by introducing a dynamic attention mechanism into the BiLSTM-LSTM encoderdecoder. With the selected informative samples annotated, the proposed NER
model is retrained. As a result, the performance of the NER model is incrementally enhanced with low labeling cost. Experimental results show the effectiveness
of the proposed method.},
DOI = {10.32604/cmc.2020.012023}
}