API2Vec++: Boosting API Sequence Representation for Malware Detection and Classification-Reference-Cited by-同舟云学术

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API2Vec++: Boosting API Sequence Representation for Malware Detection and Classification

Published:2024-08 Issue:8 Volume:50 Page:2142-2162
ISSN:0098-5589
Container-title:IEEE Transactions on Software Engineering
language:
Short-container-title:IIEEE Trans. Software Eng.

Author:

Cui Lei¹^ORCID,Yin Junnan¹^ORCID,Cui Jiancong²^ORCID,Ji Yuede³^ORCID,Liu Peng⁴^ORCID,Hao Zhiyu¹^ORCID,Yun Xiaochun¹^ORCID

Affiliation:

1. Zhongguancun Laboratory, Beijing, China

2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

3. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA

4. Guangxi Normal University, Guangxi, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/32/10636961/10586831.pdf?arnumber=10586831

Reference93 articles.

1. Defining malware: FAQ;Moir,2024

2. Ransomware attacks: detection, prevention and cure

3. Learning and Classification of Malware Behavior

4. Dynamic Malware Analysis in the Modern Era—A State of the Art Survey

5. A Multi-Perspective malware detection approach through behavioral fusion of API call sequence

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