A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

Cybersecurity

Table 5 Summary of deep learning model techniques

Model	Learning Model	Input Data	Characteristics
FCNN	Supervised	Image, sound, etc.	- No special assumptions needed to be made about the input. -Requires a huge number of connections and network parameters.
RNN	Supervised	Serial, time-series	-Processes sequences of data through internal data. -Useful in IDS with time-dependent
GAN	Semi-supervised	various	-The GAN sets up a supervised learning problem to do unsupervised learning. -Less connection.
CNN	Supervised	Image, sound, etc.	-Need a large training dataset.
Autoencoder	Unsupervised	various	-It can be trained in an unsupervised manner. -It can be used for intrusion detection in the event of a poor reconstruction. - Generating new content - Filtering out noise