A deep learning approach for host-based cryptojacking malware detection-Reference-Cited by-同舟云学术

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A deep learning approach for host-based cryptojacking malware detection

Published:2023-08-19 Issue:1 Volume:15 Page:41-56
ISSN:1868-6478
Container-title:Evolving Systems
language:en
Short-container-title:Evolving Systems

Author:

Sanda Olanrewaju,Pavlidis Michalis^ORCID,Polatidis Nikolaos

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s12530-023-09534-9.pdf

Reference42 articles.

1. Ahmad A, Shafiuddin W, Kama MN, Saudi MM (2019) A New Cryptojacking Malware Classifier Model Based on Dendritic Cell Algorithm. ACM International Conference Proceeding Series, 0–4. https://doi.org/10.1145/3387168.3387218

2. Anderson HS, Roth P (2018) EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models. http://arxiv.org/abs/1804.04637

3. Anjum MM, Iqbal S, Hamelin B (2022) ANUBIS: A Provenance Graph-Based Framework for Advanced Persistent Threat Detection. Proceedings of the ACM Symposium on Applied Computing, 1684–1693. https://doi.org/10.1145/3477314.3507097

4. Aponte-Novoa FA, Povedano Álvarez D, Villanueva-Polanco R, Sandoval Orozco AL, García Villalba LJ (2022) On detecting cryptojacking on websites: revisiting the use of classifiers. Sensors 22(23):1–15. https://doi.org/10.3390/s22239219

5. Arp D, Spreitzenbarth M, Hübner M, Gascon H, Rieck K (2014) Drebin: Effective and Explainable Detection of Android Malware in Your Pocket.

Cited by 1 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. How to evade modern web cryptojacking detection tools? A review of practical findings;Proceedings of the 19th International Conference on Availability, Reliability and Security;2024-07-30

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