On the Effectiveness of Perturbations in Generating Evasive Malware Variants-Reference-Cited by-同舟云学术

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On the Effectiveness of Perturbations in Generating Evasive Malware Variants

Published:2023 Issue: Volume:11 Page:31062-31074
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Jin Beomjin¹^ORCID,Choi Jusop²^ORCID,Hong Jin B.³^ORCID,Kim Hyoungshick¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea

2. Digital Transformation Response Support Department, Financial Security Institute, Yongin, South Korea

3. Department of Computer Science and Software Engineering, The University of Western Australia, Perth, WA, Australia

Funder

Korea Internet and Security Agency

Institute of Information and Communications Technology Planning and Evaluation

Institute for Information & communications Technology Planning & Evaluation

Korea government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10082919.pdf?arnumber=10082919

Reference35 articles.

2. Towards automated malware creation

3. Adversarial malware sample generation method based on the prototype of deep learning detector

4. Peeler: Profiling Kernel-Level Events to Detect Ransomware

5. Evading Anti-Malware Engines With Deep Reinforcement Learning

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning-powered malware detection in cyberspace: a contemporary review;Frontiers in Physics;2024-03-28

2. A survey of strategy-driven evasion methods for PE malware: Transformation, concealment, and attack;Computers & Security;2024-02

3. An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection;Computer Modeling in Engineering & Sciences;2024

4. ACMFNN: A Novel Design of an Augmented Convolutional Model for Intelligent Cross-Domain Malware Localization via Forensic Neural Networks;IEEE Access;2023

5. YAMME: a YAra-byte-signatures Metamorphic Mutation Engine;IEEE Transactions on Information Forensics and Security;2023

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