Robust identification of email tracking: A machine learning approach-Reference-Cited by-同舟云学术

Robust identification of email tracking: A machine learning approach

Published:2018-11 Issue:1 Volume:271 Page:341-356
ISSN:0377-2217
Container-title:European Journal of Operational Research
language:en
Short-container-title:European Journal of Operational Research

Author:

Haupt Johannes,Bender Benedict,Fabian Benjamin^ORCID,Lessmann Stefan^ORCID

Publisher

Elsevier BV

Subject

Information Systems and Management,Management Science and Operations Research,Modelling and Simulation,General Computer Science,Industrial and Manufacturing Engineering

Reference55 articles.

1. Gathering and mining information from web log files;Agosti,2007

2. Detecting web bugs with bugnosis: privacy advocacy through education;Alsaid,2002

3. Barret, B. (2015). A clever way to tell which of your emails are being tracked. Wired. Retrieved from https://www.wired.com/2015/03/ugly-mail/

4. E-mail tracking: status quo and novel countermeasures;Bender,2016

5. Improved phishing detection using model-based features;Bergholz,2008

Cited by 12 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. A fused large language model for predicting startup success;European Journal of Operational Research;2024-09

2. Sorting the Digital Stream: Big Data-driven Insights into Email Classification for Spam and Ham Detection;2023 IEEE International Conference on Big Data (BigData);2023-12-15

3. Artificial Intelligence Methods in Email Marketing—A Survey;Lecture Notes in Networks and Systems;2023

4. A Multiple Salient Features-Based User Identification across Social Media;Entropy;2022-04-01

5. Mutual information-based multi-output tree learning algorithm;Intelligent Data Analysis;2021-10-29