irondetect - a IF-MAP based detection engine

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The Trust@FHH team would like to announce that our IF-MAP based detection engine, irondetect, is available to the public via our Github account. Based on contexts, signatures and anomalies, irondetect is able to detect deviations from normal behavior in a IF-MAP based network.

The development was done within the ESUKOM project. irondetect is IF-MAP 2.1 compliant, but works on metadata specified by the ESUKOM project, which uses Features and Categories to structurize metadata.

In this first release, irondetect supports the following functionality:

  • Detection of abnormal behavior via Anomalies.

  • Anomaly detection uses a training phase to record the “normal” behavior.

  • Signatures allow for simple pattern matching of Features.

  • irondetect uses Contexts to further constrain, when specific signatures and anomalies are valid. Contexts can be the time, (geo) location or other parameters, that define the “situation” when a Feature was measured.

  • It can be controlled via a policy language, consisting of Rules with Conditions and Actions.

  • Detection results are published back into the MAP server (both as ESUKOM and IF-MAP Standard metadata) so other components - and irondetect itself - can react on them.

At the moment, the release comes more or less without a user documentation; you can use our demo environment irondemo (also available at Github) and take a look at the provided policy of scenario 1. Our ifmapcli tools also provide some tools to publish metadata that uses the ESUKOM metadata model.

We will release a specific irondetect documentation as well as more sophisticated example policies and scenarios for irondemo in the future.

If you have any comments or questions, please contact us at or directly create an Issue at the corresponding Github-project page.

24 Oct 2013
Hochschule Hannover
University of Applied Sciences and Arts
Faculty IV, Dept. of Computer Science
Ricklinger Stadtweg 120
30459 Hannover, Germany
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