Dataset Analysis for Anomaly Detection on Critical Infrastructures

German Lopez-Civera, Enrique de la Hoz, C Callegari, M VanSinderen, P Sarigiannidis, P Samarati, E Cabello, P Lorenz, and MS Obaidat

DCNET: Proceedings of the 13th International Joint Conference on E-Business and Telecommunications - Vol. 1, 2016 ICETE

Abstract

Anomaly Detection techniques allow to create robust security measures that provides early detection and are able to identify novel attacks that could not be prevented otherwise. Datasets represent a critical component in the process of designing and evaluating any kind of anomaly detection method. For this reason, in this paper we present the evaluation of two datasets showing the dependencies that arise between the techniques employed and the dataset itself. We also describe the characteristics that have to be taken into account while selecting a dataset to evaluate a detection algorithm in a critical infrastructure context.

How to cite

@inproceedings{lopezcivera2016dataset,
  title     = {Dataset Analysis for Anomaly Detection on Critical Infrastructures},
  author    = {German Lopez-Civera and Enrique de la Hoz and C Callegari and M VanSinderen and P Sarigiannidis and P Samarati and E Cabello and P Lorenz and MS Obaidat},
  year      = {2016},
  booktitle = {DCNET: Proceedings of the 13th International Joint Conference on E-Business and Telecommunications - Vol. 1},
  doi       = {10.5220/0006017701510158},
}

Metadata from ORCID and Google Scholar; all publications.