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Author(s):
Blenk, Andreas 
Title:
Adversarial Network Benchmarking: A Data-Driven Approach 
Abstract:
Communication networks have not only become a critical infrastructure of our digital society, but are also increasingly complex and hence error-prone. This has recently motivated the study of more automated and “self-driving” networks: networks which measure, analyze, and control themselves in an adaptive manner, reacting to changes in the environment. In particular, such networks hence require mechanisms to evaluate potential solutions to problems. However, evaluating solutions is interestingly...    »
 
Published as:
Invited presentation at Applied Machine Learning Days at EPFL, January 25-29, 2020, Lausanne, Switzerland 
Month:
Jan 
Year:
2020