  Detecting anomalies at the time of happening is vital in environments like
buildings and homes to identify potential cyber-attacks. This paper discussed
the various mechanisms to detect anomalies as soon as they occur. We shed light
on crucial considerations when building machine learning models. We constructed
and gathered data from multiple self-build (DIY) IoT devices with different
in-situ sensors and found effective ways to find the point, contextual and
combine anomalies. We also discussed several challenges and potential solutions
when dealing with sensing devices that produce data at different sampling rates
and how we need to pre-process them in machine learning models. This paper also
looks at the pros and cons of extracting sub-datasets based on environmental
conditions.
