Model-driven software engineering in practice: privacy-enhanced filtering of network traffic
Abstract
Network traffic data contains a wealth of information for use in security analysis and application development. Unfortunately, it also usually contains confidential or otherwise sensitive information, prohibiting sharing and analysis. Existing automated anonymization solutions are hard to maintain and tend to be outdated.
BibTeX
@inproceedings{vanDijk-al:FSE17,
author = {Roel van Dijk and
Christophe Creeten and
Jeroen van der Ham and
Jeroen van den Bos},
title = {Model-driven software engineering in practice: privacy-enhanced filtering of network traffic},
booktitle = {{ESEC/SIGSOFT} {FSE}},
pages = {860--865},
publisher = {{ACM}},
year = {2017},
}