Quality In / Quality Out: Assessing Data quality in an Anomaly Detection
  Benchmark
José Camacho Katarzyna Wasielewska Marta Fuentes-García Rafael Rodríguez-Gómez
Autonomous or self-driving networks are expected to provide a solution to the
myriad of extremely demanding new applications in the Future Internet. The key
to handle complexity is to perform tasks like network optimization and failure
recovery with minimal human supervision. For this purpose, the community relies
on the development of new Machine Learning (ML) models and techniques. However,
ML can only be as good as the data it is fitted with. Datasets provided to the
community as benchmarks for research purposes, which have a relevant impact in
research findings and directions, are often assumed to be of good quality by
default. In this paper, we show that relatively minor modifications on the same
benchmark dataset (UGR'16, a flow-based real-traffic dataset for anomaly
detection) cause significantly more impact on model performance than the
specific ML technique considered. To understand this finding, we contribute a
methodology to investigate the root causes for those differences, and to assess
the quality of the data labelling. Our findings illustrate the need to devote
more attention into (automatic) data quality assessment and optimization
techniques in the context of autonomous networks.