Title
Towards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current TrendsVersion
http://purl.org/coar/version/c_970fb48d4fbd8a85
Rights
© 2017 Mikel Iturbe et al.Access
http://purl.org/coar/access_right/c_abf2Publisher’s version
https://doi.org/10.1155/2017/9150965Published at
Security and Communication Networks Vol. 2017. Article ID 9150965. November, 2017Publisher
The Wiley Hindawi PartnershipAbstract
Industrial Networks (INs) are widespread environments where heterogeneous devices collaborate to control and monitor physical
processes. Some of the controlled processes belong to Critical Infrastruc ... [+]
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. [-]
xmlui.dri2xhtml.METS-1.0.item-sponsorship
This work has been developed by the Intelligent Systems for Industrial Systems group supported by the Department of Education, Language Policy and Culture of the Basque Government. This work has been partially funded by the European Union’s Horizon 2020 research and innovation programme project PROPHESY, under Grant Agreement no. 766994.xmlui.dri2xhtml.METS-1.0.item-projectID
info:eu-repo/grantAgreement/EC/H2020/766994/EU/Platform for rapid deployment of self-configuring and optimized predictive maintenance services/PROPHESYCollections
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