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 ![]() | |
Garcia, Sebastian [Czech Technical University Prague - CTU rague] | |
Verwer, Sicco [Delft University of Technology - TU Delft > Cyber Security Group] | |
State, Radu ![]() | |
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 |
There is no file associated with this reference.
All documents in ORBilu are protected by a user license.