  Data Distribution Service (DDS) is an innovative approach towards
communication in ICS/IoT infrastructure and robotics. Being based on the
cross-platform and cross-language API to be applicable in any computerised
device, it offers the benefits of modern programming languages and the
opportunities to develop more complex and advanced systems. However, the DDS
complexity equally increases its vulnerability, while the existing security
measures are limited to plug-ins and static rules, with the rest of the
security provided by third-party applications and operating system.
Specifically, traditional intrusion detection systems (IDS) do not detect any
anomalies in the publish/subscribe method. With the exponentially growing
global communication exchange, securing DDS is of the utmost importance to
futureproofing industrial, public, and even personal devices and systems. This
report presents an experimental work on the simulation of several specific
attacks against DDS, and the application of Deep Learning for their detection.
The findings show that even though Deep Learning allows to detect all simulated
attacks using only metadata analysis, their detection level varies, with some
of the advanced attacks being harder to detect. The limitations imposed by the
attempts to preserve privacy significantly decrease the detection rate. The
report also reviews the drawbacks and limitations of the Deep Learning approach
and proposes a set of selected solutions and configurations, that can further
improve the DDS security.
