Reliable Machine Learning for Networking: Key Concerns and Approaches

Hammerschmidt, Christian; Garcia, Sebastian; Verwer, Sicco; State, Radu

Reference : Reliable Machine Learning for Networking: Key Concerns and Approaches


	Document type :	Scientific congresses, symposiums and conference proceedings : Poster
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/32813

Title :	Reliable Machine Learning for Networking: Key Concerns and Approaches
Language :	English
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) >]
Publication date :	Oct-2017
Peer reviewed :	Yes
Audience :	International
Event name :	The 42nd IEEE Conference on Local Computer Networks (LCN)
Event date :	October 9-12, 2017
Event organizer :	IEEE
Event place (city) :	Singapore
Event country :	Singapore
Keywords :	[en] 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.
Permalink :	http://hdl.handle.net/10993/32813
FnR project :	FnR ; FNR10053360 > Christian Hammerschmidt > PAULINE > Stream Mining For Predictive Authentication Under Adversarial Influence > 01/03/2015 > 14/11/2017 > 2015

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