Reference : Reliable Machine Learning for Networking: Key Concerns and Approaches
Scientific congresses, symposiums and conference proceedings : Poster
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/32813
Reliable Machine Learning for Networking: Key Concerns and Approaches
English
Hammerschmidt, Christian mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Garcia, Sebastian [Czech Technical University Prague - CTU rague]
Verwer, Sicco [Delft University of Technology - TU Delft > Cyber Security Group]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Oct-2017
Yes
International
The 42nd IEEE Conference on Local Computer Networks (LCN)
October 9-12, 2017
IEEE
Singapore
Singapore
[en] Computer Science - Learning ; Statistics - Machine Learning
[en] Machine learning has become one of the go-to methods for solving problems in the field of networking.
This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks.
While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when using machine learning as an approach to a problem in networking and translate testing performance to real networks deployments successfully.
This paper, rather than presenting a particular technical result, discusses the necessary considerations to obtain good results when using machine learning to analyze network-related data.
http://hdl.handle.net/10993/32813
FnR ; FNR10053360 > Christian Hammerschmidt > PAULINE > Stream Mining For Predictive Authentication Under Adversarial Influence > 01/03/2015 > 14/11/2017 > 2015

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