No full text
Poster (Scientific congresses, symposiums and conference proceedings)
Reliable Machine Learning for Networking: Key Concerns and Approaches
HAMMERSCHMIDT, Christian; Garcia, Sebastian; Verwer, Sicco et al.
2017The 42nd IEEE Conference on Local Computer Networks (LCN)
 

Files


Full Text
No document available.

Send to



Details



Keywords :
Computer Science - Learning; Statistics - Machine Learning
Abstract :
[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.
Disciplines :
Computer science
Author, co-author :
HAMMERSCHMIDT, Christian ;  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  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
no
Language :
English
Title :
Reliable Machine Learning for Networking: Key Concerns and Approaches
Publication date :
October 2017
Event name :
The 42nd IEEE Conference on Local Computer Networks (LCN)
Event organizer :
IEEE
Event place :
Singapore, Singapore
Event date :
October 9-12, 2017
Audience :
International
Focus Area :
Computational Sciences
FnR Project :
FNR10053360 - Stream Mining For Predictive Authentication Under Adversarial Influence, 2015 (01/03/2015-14/11/2017) - Christian Hammerschmidt
Available on ORBilu :
since 05 November 2017

Statistics


Number of views
157 (3 by Unilu)
Number of downloads
0 (0 by Unilu)

Bibliography


Similar publications



Contact ORBilu