Enhancing Malware Classification via Self-Similarity Techniques-Reference-Cited by-同舟云学术

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Enhancing Malware Classification via Self-Similarity Techniques

Published:2024 Issue: Volume:19 Page:7232-7244
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Zhong Fangtian¹^ORCID,Hu Qin²^ORCID,Jiang Yili²^ORCID,Huang Jiaqi³^ORCID,Zhang Cheng⁴^ORCID,Wu Dinghao⁵

Affiliation:

1. Gianforte School of Computing, Montana State University, Bozeman, MT, USA

2. Department of Computer Science, Georgia State University, Atlanta, GA, USA

3. Department of Computer Science and Cybersecurity, University of Central Missouri, Warrensburg, MO, USA

4. Department of Computer Information and Decision Management, West Texas A&M University, Canyon, TX, USA

5. College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, USA

Funder

startup funds from Montana State University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/10206/10319981/10609414.pdf?arnumber=10609414

Reference51 articles.

1. Static Analysis of Executables to Detect Malicious Patterns

2. Discovering flaws in security-focused static analysis tools for Android using systematic mutation;Bonett

3. Revisiting static analysis of Android malware;Gagnon

4. Humans vs. machines in malware classification;Aonzo

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