Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012 |
| Publisher | Institute of Electrical and Electronic Engineers IEEE |
| Pages | 771-774 |
| ISBN (Electronic) | 978-0-7695-4865-4 |
| ISBN (Print) | 978-1-4673-5146-1 |
| DOIs | |
| Publication status | Published - 2012 |
| MoE publication type | Not Eligible |
| Event | IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012 - Besancon, France Duration: 20 Nov 2012 → 23 Nov 2012 |
Conference
| Conference | IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012 |
|---|---|
| Abbreviated title | GreenCom 2012 |
| Country | France |
| City | Besancon |
| Period | 20/11/12 → 23/11/12 |
Fingerprint
Cite this
}
Feature selection for machine learning based anomaly detection in industrial control system networks. / Mantere, Matti; Sailio, Mirko; Noponen, Sami.
Proceedings: IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012. Institute of Electrical and Electronic Engineers IEEE, 2012. p. 771-774.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Feature selection for machine learning based anomaly detection in industrial control system networks
AU - Mantere, Matti
AU - Sailio, Mirko
AU - Noponen, Sami
PY - 2012
Y1 - 2012
N2 - The nature of the traffic in industrial control system network is markedly different from more open networks. Industrial control system networks should be far more restricted in what types of traffic diversity is present. This enables the usage of approaches that are currently not as feasible in open environments, such as machine learning based anomaly detection. Without proper customization for the special requirements of industrial control system network environment many existing anomaly or misuse detection systems will perform sub-optimally. Machine learning based approach would reduce the amount of manual customization required for different restricted network environments of which an industrial control system network is an good example of. In this paper we present an initial analysis of data received from a ethernet network of a live running industrial site. This includes both control data and the data flowing between the control network and the office network. A set of possible features to be used for detecting anomalies is studied for this environment.
AB - The nature of the traffic in industrial control system network is markedly different from more open networks. Industrial control system networks should be far more restricted in what types of traffic diversity is present. This enables the usage of approaches that are currently not as feasible in open environments, such as machine learning based anomaly detection. Without proper customization for the special requirements of industrial control system network environment many existing anomaly or misuse detection systems will perform sub-optimally. Machine learning based approach would reduce the amount of manual customization required for different restricted network environments of which an industrial control system network is an good example of. In this paper we present an initial analysis of data received from a ethernet network of a live running industrial site. This includes both control data and the data flowing between the control network and the office network. A set of possible features to be used for detecting anomalies is studied for this environment.
U2 - 10.1109/GreenCom.2012.127
DO - 10.1109/GreenCom.2012.127
M3 - Conference article in proceedings
SN - 978-1-4673-5146-1
SP - 771
EP - 774
BT - Proceedings
PB - Institute of Electrical and Electronic Engineers IEEE
ER -