  Denial of service attacks are especially pertinent to the internet of things
as devices have less computing power, memory and security mechanisms to defend
against them. The task of mitigating these attacks must therefore be redirected
from the device onto a network monitor. Network intrusion detection systems can
be used as an effective and efficient technique in internet of things systems
to offload computation from the devices and detect denial of service attacks
before they can cause harm. However the solution of implementing a network
intrusion detection system for internet of things networks is not without
challenges due to the variability of these systems and specifically the
difficulty in collecting data. We propose a model-hybrid approach to model the
scale of the internet of things system and effectively train network intrusion
detection systems. Through bespoke datasets generated by the model, the IDS is
able to predict a wide spectrum of real-world attacks, and as demonstrated by
an experiment construct more predictive datasets at a fraction of the time of
other more standard techniques.
