A survey of malware behavior description and analysis-Reference-Cited by-同舟云学术

A survey of malware behavior description and analysis

Published:2018-05 Issue:5 Volume:19 Page:583-603
ISSN:2095-9184
Container-title:Frontiers of Information Technology & Electronic Engineering
language:en
Short-container-title:Frontiers Inf Technol Electronic Eng

Author:

Yu Bo^ORCID,Fang Ying,Yang Qiang,Tang Yong,Liu Liu

Funder

National Natural Science Foundation of China

Publisher

Zhejiang University Press

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing

Link

http://link.springer.com/content/pdf/10.1631/FITEE.1601745.pdf

Reference117 articles.

1. Alam S, Horspool RN, Traore I, et al., 2015. A framework for metamorphic malware analysis and real-time detection. Comput Secur, 48:212–233. https://doi.org/10.1016/j.cose.2014.10.011

2. Alazab M, 2015. Profiling and classifying the behavior of malicious codes. J Syst Softw, 100:91–102. https://doi.org/10.1016/j.jss.2014.10.031

3. Alazab M, Venkataraman S, Watters P, 2010. Towards Understanding malware behaviour by the extraction of API calls. Proc 2nd Cybercrime and Trustworthy Computing Workshop, p.52–59. https://doi.org/10.1109/CTC.2010.8

4. Anderson B, Storlie C, Lane T, 2012. Improving malware classification: Bridging the static/dynamic gap. Proc 5th ACM Workshop on Security and Artificial Intelligence, p.3–14. https://doi.org/10.1145/2381896.2381900

5. Anderson B, Lane T, Hash C, 2014. Malware phylogenetics based on the multiview graphical lasso. Proc 13th Int Symposium on Advances in Intelligent Data Analysis XIII, p.1–12. https://doi.org/10.1007/978-3-319-12571-8_1

Cited by 37 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. Hybrid Android Malware Detection: A Review of Heuristic-Based Approach;IEEE Access;2024

2. Detecting IoT Malware Using Federated Learning;Lecture Notes in Networks and Systems;2024

3. Efficient Deep Learning Method for Detection of Malware Attacks in Internet of Things Networks;Communications in Computer and Information Science;2024

4. Using deep graph learning to improve dynamic analysis-based malware detection in PE files;Journal of Computer Virology and Hacking Techniques;2023-10-20

5. Android Malware Detection Using Incremental Learning Approach;2023 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom);2023-07-04