Characterizing the Internet Host Population Using Deep Learning-Reference-Cited by-同舟云学术

Characterizing the Internet Host Population Using Deep Learning

Published:2018-10-31 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Internet Measurement Conference 2018
language:
Short-container-title:

Author:

Sarabi Armin¹,Liu Mingyan¹

Affiliation:

1. University of Michigan, Ann Arbor, MI, USA

Funder

National Science Foundation

U.S. Department of Homeland Security

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3278532.3278545

Reference42 articles.

1. Phishing detection based Associative Classification data mining

2. PhishZoo: Detecting Phishing Websites by Looking at Them

3. Matthew James Beal. 2003. Variational Algorithms for Approximate Bayesian Inference. University of London. Matthew James Beal. 2003. Variational Algorithms for Approximate Bayesian Inference. University of London.

Cited by 13 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. Unraveling Threat Intelligence Through the Lens of Malicious URL Campaigns;Proceedings of the 18th Asian Internet Engineering Conference;2023-12-12

2. An LLM-based Framework for Fingerprinting Internet-connected Devices;Proceedings of the 2023 ACM on Internet Measurement Conference;2023-10-24

3. Dizzy: Large-Scale Crawling and Analysis of Onion Services;Proceedings of the 18th International Conference on Availability, Reliability and Security;2023-08-29

4. Detecting network-based internet censorship via latent feature representation learning;Computers & Security;2023-05

5. Detecting and Interpreting Changes in Scanning Behavior in Large Network Telescopes;IEEE Transactions on Information Forensics and Security;2022