MITREtrieval: Retrieving MITRE Techniques From Unstructured Threat Reports by Fusion of Deep Learning and Ontology-Reference-Cited by-同舟云学术

MITREtrieval: Retrieving MITRE Techniques From Unstructured Threat Reports by Fusion of Deep Learning and Ontology

Published:2024-08 Issue:4 Volume:21 Page:4871-4887
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

Huang Yi-Ting¹^ORCID,Vaitheeshwari R.²^ORCID,Chen Meng-Chang³^ORCID,Lin Ying-Dar⁴^ORCID,Hwang Ren-Hung⁵,Lin Po-Ching⁶^ORCID,Lai Yuan-Cheng⁷^ORCID,Wu Eric Hsiao-Kuang²^ORCID,Chen Chung-Hsuan²,Liao Zi-Jie²,Chen Chung-Kuan⁸

Affiliation:

1. Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

2. Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan

3. Institute of Information Science, Academia Sinica, Taipei, Taiwan

4. Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

5. College of Artificial Intelligence, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

6. Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan

7. Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan

8. Research Division, CyCraft Technology, New Taipei City, Taiwan

Funder

Ministry of Science and Technology, Taiwan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4275028/10643497/10539631.pdf?arnumber=10539631

Reference54 articles.

1. The Big Four - What We Did Wrong in Advanced Persistent Threat Detection?

2. TTPDrill

3. Using Entropy and Mutual Information to Extract Threat Actions from Cyber Threat Intelligence

4. Automatic identification of indicators of compromise using neural-based sequence labelling;Zhou;arXiv:1810.10156,2018

5. ChainSmith: Automatically Learning the Semantics of Malicious Campaigns by Mining Threat Intelligence Reports