Encrypted Malware Traffic Detection Using TLS Features and Random Forest-Reference-Cited by-同舟云学术

Encrypted Malware Traffic Detection Using TLS Features and Random Forest

Published:2021 Issue: Volume: Page:85-100
ISSN:2211-0984
Container-title:Computational and Experimental Simulations in Engineering
language:
Short-container-title:

Author:

Lee Weilong,Jin Shuyuan

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-67090-0_8

Reference24 articles.

1. Google. HTTPS encryption on the web [EB/OL]. https://transparencyreport.google.com/https/overview?hl=en. Accessed 3 Apr 2018

2. Deepen Desai. SSL/TLS-based malware attacks [EB/OL]. https://www.zscaler.com/blogs/research/ssltls-based-malware-attacks. Accessed 08 Aug 2017/05 Apr 2018

3. Karagiannis, T., Broido, A., Brownlee, N., et al.: Is P2P dying or just hiding? [P2P traffic measurement]. In: Global Telecommunications Conference, 2004. GLOBECOM 2004, vol. 3, pp. 532–1538. IEEE. (2004)

4. Duffield, N.G., et al.: Statistical, signature-based approach to IP traffic classification. U.S. Patent No. 7,660,248. 9 February 2010

5. Li, J., Zhang, S., Ye, X., et al.: Identifying skype traffic by random forest. In: International Conference on Wireless Communications, NETWORKING and Mobile Computing, pp. 2841–2844. IEEE (2007)

Cited by 3 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. MVDet: Encrypted malware traffic detection via multi-view analysis;Journal of Computer Security;2024-02-12

2. CBSeq: A Channel-Level Behavior Sequence for Encrypted Malware Traffic Detection;IEEE Transactions on Information Forensics and Security;2023

3. Light-weight Unsupervised Anomaly Detection for Encrypted Malware Traffic;2022 7th IEEE International Conference on Data Science in Cyberspace (DSC);2022-07