Skip to Main content Skip to Navigation
Conference papers

Towards Learning Normality for Anomaly Detection in Industrial Control Networks

Abstract : Recent trends in automation technology lead to a rising exposition of industrial control systems (ICS) to new vulnerabilities. This requires the introduction of proper security approaches in this field. Prevalent in ICS is the use of access control. Especially in critical infrastructures, however, preventive security measures should be complemented by reactive ones, such as intrusion detection. Beginning from the characteristics of automation networks we outline the implications for a suitable application of intrusion detection in this field. On this basis, an approach for creation of self-learning anomaly detection for ICS protocols is presented. In contrast to other approaches, it takes all network data into account: flow information, application data, and the packet order. We discuss the challenges that have to be solved in each step of the network data analysis to identify future aspects of research towards learning normality in industrial control networks.
Complete list of metadatas

Cited literature [23 references]  Display  Hide  Download

https://hal.inria.fr/hal-01489971
Contributor : Hal Ifip <>
Submitted on : Tuesday, March 14, 2017 - 5:06:32 PM
Last modification on : Tuesday, March 14, 2017 - 5:12:28 PM
Document(s) archivé(s) le : Thursday, June 15, 2017 - 2:50:14 PM

File

978-3-642-38998-6_8_Chapter.pd...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Franka Schuster, Andreas Paul, Hartmut König. Towards Learning Normality for Anomaly Detection in Industrial Control Networks. 7th International Conference on Autonomous Infrastructure (AIMS), Jun 2013, Barcelona, Spain. pp.61-72, ⟨10.1007/978-3-642-38998-6_8⟩. ⟨hal-01489971⟩

Share

Metrics

Record views

121

Files downloads

612