Hammerschmidt, C. A., Garcia, S., Verwer, S., & State, R. (2017). Reliable Machine Learning for Networking: Key Issues and Approaches. In 2017 IEEE 42nd Conference on Local Computer Networks (LCN) (pp. 167-170). IEEE. doi: 10.1109/LCN.2017.74
Abstract
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.
