Computer Science ›› 2020, Vol. 47 ›› Issue (2): 245-250.doi: 10.11896/jsjkx.190500063

• Information Security • Previous Articles     Next Articles

Malware Name Recognition in Tweets Based on Enhanced BiLSTM-CRF Model

GU Xue-mei1,LIU Jia-yong1,CHENG Peng-sen1,2,HE Xiang1   

  1. (School of Cybersecurity,Sichuan University,Chengdu 610000,China)1;
    (Key Laboratory of Network Assessment Technology,Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100093,China)2
  • Received:2019-05-14 Online:2020-02-15 Published:2020-03-18
  • About author:GU Xue-mei,born in 1995,postgraduate,is not member of China Computer Federation (CCF).Her main research interests include information content security and so on;LIU Jia-yong,born in 1962,Ph.D,professor,Ph.D supervisor,is not member of China Computer Federation (CCF).His main research interests include information security,and network communication and security.
  • Supported by:
    This work was supported by Open Research Fund of the Key Laboratory of Network Assessment Technology of Chinese Academy of Sciences (NST-18-001).

Abstract: To address the problems such as short,informal,single entity category and entity disambiguation in the malware name recognition task on Twitter,this paper proposed an entity recognition method based on BERT-BiLSTM-Self-attention-CRF to automatically recognize malware name in tweets.Based on the BiLSTM-CRF model,the BERT is used to encode context information,improve the contextual semantic quality of word embeddings,and enhance the semantic disambiguation ability.At the same time,Self-attention mechanism is used to learn weighted representation to improve the performance of single entity category re-cognition by learning the long-term relations between words and sentence structure.To evaluate the proposed methods,this paper constructed a labeled dataset in tweets that contains malware name entities.Experimental results show that the proposed method can achieve a better performance,attain 86.38% precision,84.73% recall and 85.55% F-score.The proposed model can outperforms the baseline model,with F-score improved by 12.61%.

Key words: Malware name recognition, Entity disambiguation, Dynamic word embedding, Class imbalance, Importance weighting

CLC Number: 

  • TP391
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