MalwareTotal: Multi-Faceted and Sequence-Aware Bypass Tactics against Static Malware Detection-Reference-Cited by-同舟云学术

MalwareTotal: Multi-Faceted and Sequence-Aware Bypass Tactics against Static Malware Detection

Published:2024-04-12 Issue: Volume:2315 Page:1-12
ISSN:
Container-title:Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
language:
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Author:

He Shuai¹²³^ORCID,Fu Cai¹²³^ORCID,Hu Hong⁴^ORCID,Chen Jiahe¹^ORCID,Lv Jianqiang¹^ORCID,Jiang Shuai¹^ORCID

Affiliation:

1. School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

2. Hubei Key Laboratory of Distributed System Security, Huazhong University of Science and Technology, Wuhan, China

3. Hubei Engineering Research Center on Big Data Security, Huazhong University of Science and Technology, Wuhan, China

4. Pennsylvania State University, State College, Pennsylvania, USA

Funder

National Key R&D Plan of China

National Natural Science Foundation of China

CCF-NSFOCUS Kunpeng Research Fund

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3597503.3639141

Reference72 articles.

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3. Hyrum S Anderson, Anant Kharkar, Bobby Filar, and Phil Roth. 2017. Evading machine learning malware detection. Black Hat (2017).

4. Hyrum S Anderson and Phil Roth. 2018. Ember: an open dataset for training static pe malware machine learning models. arXiv preprint arXiv:1804.04637 (2018).

5. Kshitiz Aryal Maanak Gupta and Mahmoud Abdelsalam. 2022. A Survey on Adversarial Attacks for Malware Analysis. arXiv:2111.08223 [cs.CR]