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Security and Communication Networks
Volume 2017 (2017), Article ID 9150965, 17 pages
https://doi.org/10.1155/2017/9150965
Review Article

Towards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current Trends

Department of Electronics and Computing, Mondragon Unibertsitatea, Goiru 2, 20500 Arrasate-Mondragón, Spain

Correspondence should be addressed to Mikel Iturbe

Received 13 September 2017; Accepted 5 November 2017; Published 22 November 2017

Academic Editor: Javier Lopez

Copyright © 2017 Mikel Iturbe et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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 Big Data frameworks such as Hadoop 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 IN-based 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.