2026-04-21T19:26:11Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/11112024-03-06T08:38:58Zcom_20.500.11984_473col_20.500.11984_478

Iturbe, Mikel Garitano, Iñaki Zurutuza, Urko Uribeetxeberria, Roberto 2018-10-25T14:16:09Z 2018-10-25T14:16:09Z 2017 1939-0122 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=128631 https://hdl.handle.net/20.500.11984/1111 Industrial Networks (INs) are widespread environments where heterogeneous devices collaborate to control and monitor physical processes. Some of the controlled processes belong to Critical Infrastructures (CIs), and, as such, IN protection is an active research field. Among different types of security solutions, IN Anomaly Detection Systems (ADSs) have received wide attention from the scientific community.While INs have grown in size and in complexity, requiring the development of novel, Big Data solutions for data processing, IN ADSs have not evolved at the same pace. In parallel, the development of BigData frameworks such asHadoop or Spark has led the way for applying Big Data Analytics to the field of cyber-security,mainly focusing on the Information Technology (IT) domain. However, due to the particularities of INs, it is not feasible to directly apply IT security mechanisms in INs, as IN ADSs face unique characteristics. In this work we introduce three main contributions. First, we survey the area of Big Data ADSs that could be applicable to INs and compare the surveyed works. Second, we develop a novel taxonomy to classify existing INbased ADSs. And, finally, we present a discussion of open problems in the field of Big Data ADSs for INs that can lead to further development. eng Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ © 2017 Mikel Iturbe et al. Towards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current Trends http://purl.org/coar/resource_type/c_6501